project 1
Derryq writer
isthisdoable.zipProject 1 instructions.docx
1. Formulas
You are responsible for tracking daily sales. The table on the formulas worksheet lists a few of the transactions for your company. Notice that the sales tax amount and transaction totals are not filled in. Complete the tasks to complete the table.
1.1
Construct a formula in cell D5 to calculate the sales tax amount for transaction 578. Be sure to appropriately reference the transaction amount in cell C5 and the sales tax rate in cell C2 so your formula can be reused for the remaining transactions.
1.2
Copy the formula you used in cell D5 down to calculate the sales tax amount for the remaining transactions.
1.3
Construct a formula in cell E5 to calculate the total amount for transaction 578. Be sure to appropriately reference the transaction amount in cell C5 and the sales tax amount in cell D5 so you can reuse your formula to calculate the total for the remaining transactions.
1.4
Copy the formula you used in cell E5 down to calculate the total for the remaining transactions.
1.5
Use the SUM function to calculate the grand total for all the transactions in cell E18.
2. Statistical Functions
There are 30 Major League Baseball (MLB) teams. The table on the statistical functions worksheet lists the 2019 and 1990 payroll and win totals for each team (notice that four teams were added after the 1990 season). Some MLB fans complain because the league does little to regulate the amount of money teams can pay for salaries. They argue that the teams that spend the most money will win the most games. This would put teams from small markets (that earn less revenue) at a disadvantage. Complete the tasks by inserting your formulas (or responses) in column I for each task to see if small market teams are at a disadvantage.
2.1
Use the COUNT function to calculate the number of MLB teams in 1990 (range G4:G33).
2.2
Use the COUNTA function to calculate the number of MLB teams (use range G4:G33).
2.3
Use the SUM function to calculate the total amount of salaries paid in 1990.
2.4
Use the SUM function to calculate the total amount of salaries paid in 2019.
2.5
Use the AVERAGE function to calculate the average salary for the teams in 1990.
2.6
Use the AVERAGE function to calculate the average salary for the teams in 2019.
2.7
Use the MAX function to determine the maximum team salary amount in 1990.
2.8
Use the MIN function to determine the minimum team salary amount in 2019.
3. Finance
Complete each task by inserting the appropriate function, referencing the appropriate cells in the task data, in the specified cells.
3.1
You are interested in purchasing a home. What will your monthly payment be if you take out a $175,000 mortgage for 30 years (360 months) at 4.25% interest?
· Reference the loan information in the "Task 1 Data" cells as the arguments for your function.
· Remember to divide the interest rate by 12 (to calculate the monthly interest rate) in your function.
3.2
You are interested in purchasing a home. You can afford $1200 a month as a mortgage payment. How much can you pay for a home assuming a 30 year (360 months) loan at 4.25% interest?
· Please reference the loan information in the "Task 2 Data" cells as the arguments for your function.
· Remember to divide the interest rate by 12 (to calculate the monthly interest rate) in your function.
3.3
You are interested in purchasing a home. You have been quoted monthly payments of $950 for a 30 year mortgage. Your original loan amount is $212,000. What is the interest rate you will pay on the loan?
· Use the RATE function.
· Reference the loan information in the "Task 3 & 4 Data" cells as the arguments for your function.
· Remember to multiply the computed nominal interest rate by 12 (to calculate the annual interest rate).
3.4
What is the effective annual rate of the loan you worked with in task 3?
· Reference the nominal rate you calculated in the previous task.
3.5
You are interested in saving for a trip when you graduate in three years. You can save $75 each of the next 36 months and earn 2.75% interest on your money. How much money will you have in your savings account in 36 months for your trip?
· Reference the loan information in the "Task 5 Data" cells as the arguments for your function.
3.6
You are interested in purchasing a home. You will take out a mortgage of $310,000 to pay for the home and pay 4.5% interest. What will your monthly payment be if you take 15 years to pay off the loan?
· Reference the loan information in the "Task 6-8 Data" cells as the arguments for your function.
3.7
What will your monthly payment be if you take 30 years to pay off the loan you worked with in task 6? (Please reference the loan information in the "Task 6-8 Data" cells as the arguments for your functions.)
· Reference the loan information in the "Task 6-8 Data" cells as the arguments for your function.
3.8
How much money will you save if you pay off the loan in 15 years instead of 30 years?
· Reference the total payment amounts in cells C38 and D38 to calculate the difference.
Project 1.xlsx
Statistical Functions
| Major League Baseball Salaries by Team | Statistical Functions | |||||||||
| DIVISION | TEAM | 2019 PAYROLL | 1990 PAYROLL | 2019 WINS | 1990 Wins | Response | Task | |||
| AL East | Baltimore Orioles | $73,370,109 | $10,037,084 | 54 | 76 | Number of MLB Teams in 1990 | ||||
| AL East | Boston Red Sox | $229,196,106 | $20,983,333 | 84 | 88 | |||||
| AL East | New York Yankees | $223,019,037 | $20,991,318 | 103 | 67 | Number of MLB teams | ||||
| AL East | Tampa Bay Rays | $64,178,722 | NA | 96 | NA | |||||
| AL East | Toronto Blue Jays | $111,371,067 | $18,486,834 | 67 | 86 | Total salaries paid in 1990. | ||||
| AL Central | Chicago White Sox | $91,371,201 | $9,496,238 | 72 | 94 | |||||
| AL Central | Cleveland Indians | $107,693,747 | $15,152,000 | 93 | 77 | Total salaries paid in 2019. | ||||
| AL Central | Detroit Tigers | $114,631,137 | $18,092,238 | 47 | 79 | |||||
| AL Central | Los Angeles Angels | $161,270,385 | $21,870,000 | 72 | 80 | Average salary paid by the teams in 1990. | ||||
| AL Central | Minnesota Twins | $125,256,003 | $15,106,000 | 101 | 74 | |||||
| AL West | Kansas City Royals | $104,773,003 | $23,873,745 | 59 | 75 | Average salary paid by the teams in 2019. | ||||
| AL West | Los Angeles Dodgers | $207,000,814 | $21,618,704 | 106 | 86 | |||||
| AL West | Oakland Athletics | $93,394,531 | $19,987,501 | 97 | 103 | Maximum team salary amount in 1990. | ||||
| AL West | Seattle Mariners | $144,391,293 | $12,841,667 | 68 | 77 | |||||
| AL West | Texas Rangers | $148,538,766 | $15,104,372 | 78 | 83 | Minimum team salary amount in 2019. | ||||
| NL East | Atlanta Braves | $143,947,963 | $13,328,334 | 97 | $65 | |||||
| NL East | Houston Astros | $168,804,925 | $18,830,000 | 107 | 75 | |||||
| NL East | New York Mets | $146,335,812 | $22,418,834 | 86 | 91 | |||||
| NL East | Philadelphia Phillies | $160,192,244 | $13,953,667 | 81 | 77 | |||||
| NL East | Washington Nationals* | $172,307,808 | $16,656,388 | 93 | 85 | |||||
| NL Central | Chicago Cubs | $221,590,085 | $14,496,000 | 84 | 77 | |||||
| NL Central | Cincinnati Reds | $128,391,569 | $14,769,500 | 75 | 91 | |||||
| NL Central | Milwaukee Brewers | $135,889,019 | $20,019,167 | 89 | 74 | |||||
| NL Central | Pittsburgh Pirates | $72,731,474 | $15,656,000 | 69 | 95 | |||||
| NL Central | St. Louis Cardinals | $174,317,164 | $20,923,334 | 91 | 70 | |||||
| NL West | Arizona Diamondbacks | $118,927,905 | NA | 85 | NA | |||||
| NL West | Colorado Rockies | $157,162,629 | NA | 71 | NA | |||||
| NL West | Florida Marlins | $75,596,271 | NA | 57 | NA | |||||
| NL West | San Diego Padres | $104,254,790 | $18,588,334 | 70 | 75 | |||||
| NL West | San Francisco Giants | $178,582,126 | $20,942,333 | 77 | 85 | |||||
| * The Washington Nationals were the Montreal Expos in 1990 | ||||||||||
| NA = The team did not exist in 1990 |
Financial Functions
| Financial Functions | |||||
| Task 1 Data | Task 2 Data | ||||
| Loan Amount | -$175,000.00 | Monthly Payment | -$1,200.00 | ||
| Interest Rate* | 4.25% | Interest Rate* | 4.25% | ||
| Number of Payments | 360 | Number of Payments | 360 | ||
| Monthly Payment | Loan Amount | ||||
| * Remember to divide the interest rate by 12 (to calculate the monthly interest rate) in your function. | * Remember to divide the interest rate by 12 (to calculate the monthly interest rate) in your function. | ||||
| Task 3 and 4 Data | Task 5 Data | ||||
| Loan Amount | -$212,000.00 | Monthly Savings Amount | -$75.00 | ||
| Monthly Payment | $950.00 | Interest Rate* | 2.75% | ||
| Number of Payments | 360 | Number of Months | 36 | ||
| Nominal Interest Rate | Future Account Balance | ||||
| Effective Interest Rate | |||||
| * Remember to multiply the nominal interest rate by 12 (to calculate the annual interest rate). | * Remember to divide the interest rate by 12 (to calculate the monthly interest rate) in your function. | ||||
| Task 6-8 Data | |||||
| 15 Year Mortgage | 30 Year Mortgage | ||||
| Loan Amount | -$310,000.00 | -$310,000.00 | |||
| Interest Rate* | 4.50% | 4.50% | |||
| Number of Payments | 180 | 360 | |||
| Monthly Payment | |||||
| Total Payments (PMT * # of PMTs) | $0.00 | $0.00 | |||
| Difference between 30-year and 15-year payback (the 30 year amount - the 15 year amount) | |||||
| * Remember to divide the interest rate by 12 (to calculate the monthly interest rate) in your functions. | |||||
Formulas
| Sales Tax Rate | 6.75% | |||
| Transaction ID | Amount | Sales Tax | Total | |
| 578 | $42.00 | |||
| 579 | $167.00 | |||
| 580 | $209.00 | |||
| 581 | $142.00 | |||
| 582 | $234.00 | |||
| 583 | $88.00 | |||
| 584 | $197.00 | |||
| 585 | $209.00 | |||
| 586 | $163.00 | |||
| 587 | $151.00 | |||
| 588 | $103.00 | |||
| 589 | $148.00 | |||
| 590 | $51.00 | |||
| Grand Total |
Project 2 instructions.docx
1. Boolean Functions
An infield fly in baseball is called to prevent the defense from recording an easy double play. When an infield fly occurs, the batter is automatically out once the ball is touched by a fielder or hits the ground, and the baserunners must go back to their bases (though they may tag up if they wish).
An infield fly occurs when the following conditions are met: (1) there is a force out at third base (this means that there are runners on first base and second base), (2) there are not two outs, and (3) the batter hits a catchable fly ball to the infield or the shallow outfield. The table on the Boolean Functions worksheet highlights 30 baseball scenarios.
Complete the tasks to determine if the umpire should declare an infield fly.
1.1
Use the AND function with appropriate arguments in cell H4 to determine if there is a force out at third base.
a. There is a force out at third base if "Runner on 1st" and "Runner on 2nd" are both "Yes".
1.2
Copy your function in cell H4 and paste it down to complete the "Force at Third" column of the table.
1.3
Use the OR function with appropriate arguments in cell I4 to determine if there is a "Fly Ball".
a. There is a "Fly Ball" if a "Catchable Fly Ball is Hit to" the "Infield" (cell E4 is "Yes") or "Shallow Outfield" (cell F4 is "Yes").
1.4
Copy your function in cell I4 and paste it down to complete the "Fly Ball" column of the table.
1.5
Use the NOT function in cell J4 to determine if there are "Not 2 Outs". Use the "Outs" column in your determination.
1.6
Copy your function in cell J4 and paste it down to complete the "Not 2 Outs" column of the table.
1.7
Use the AND function in cell K4 to determine if all of the conditions are met for an infield fly to be declared. These conditions are:
a. There must be a force out at third (the value in H4 is TRUE).
b. There must be a catchable fly ball hit to the infield or shallow outfield (the value in I4 is TRUE).
c. There must not be two outs (the value in J4 is TRUE).
1.8
Copy your function in cell K4 and paste it down to complete the "Infield Fly" column of the table.
IF Function
Beverly sells donuts for $.50 each at the local bakery.
If a customer buys at least a dozen donuts, the cost is reduced $.40 each.
Beverly earns a commission based on the number of donuts she sells to a customer.
· If a sale totals more than $10, she earns 2% on that sale total.
· If a sale totals more than $5 (but is $10 or less), she earns 1% commission on that sale total.
· If a sale totals $5 or less, she does not earn a commission.
Because the bakery makes so much money on beverages, she also earns a beverage commission in addition to her donut commission. Beverly earns a 10% commission on the total sale of any order where a customer buys a beverage.
Complete the table on the IF Function worksheet to help Beverly calculate her commission based on the 50 customers she helped this morning.
2.1
Use an IF function in cell E10 to calculate the price to charge per donut for order 1.
a. Customer are charged $.50 per donut unless they buy a dozen or more. In this case, they are charged $.40 a donut.
b. Refer to the appropriate price in cells C3 and C4 for your "value_if_true" and "value_if_false" arguments.
c. Use absolute and relative references when appropriate.
2.2
Copy your formula in cell E10 and paste it down to complete the "Price/Donut" column of the table.
2.3
Use an IF function in cell H10 to calculate the commission rate for the total sale for order 1.
a. The commission rate is 2% for all sales that total more than $10. It is 1% for all sales that total more than $5, but are less than or equal to $10.
b. No commission is paid on sales that total $5 or less.
c. Reference the threshold levels (cells F5 and F6) in your logical tests for your IF function and the commission rates (cells G5 and G6) as your "value_if_true" and "value_if_false" arguments.
d. Use appropriate relative and absolute references.
2.4
Copy your formula in cell H10 and paste it down to complete the "Rate" column of the table.
2.5
Use an IF function in cell J10 to calculate the commission earned for beverages for order 1.
a. The beverage commission is 10% of the total sale if the customer buys a beverage.
b. If the beverage sale amount (cell D10) is greater than 0, the beverage commission equals the sales total (cell G10) multiplied by the beverage commission rate (cell G4).
c. Otherwise, the beverage commission is 0.
d. Use appropriate relative and absolute cell references.
2.6
Copy your formula in cell J10 and paste it down to complete the "Beverage" commission column of the table.
2.7
Beverly treats herself to a donut if her daily commission is more than her set Donut Threshold.
a. Use an IF function in cell K5 to determine if she should buy herself a donut.
b. Cell K5 should contain the value "Yes" if her total commission in cell K3 is more than her "Donut Threshold".
c. Otherwise, cell K5 should contain the value "No".
d. Be sure to reference the "Donut Threshold" value in cell K6 in your formula.
3 Lookup Functions
Built-tough Boards sells outside bulletin boards used to display community information on the outside of buildings. The company sells blue and red bulletin boards, which they will deliver regionally up to 1,000 miles. While Built-tough Boards offers a discount for purchasing in bulk, it requires customers to buy at least two bulletin boards at a time.
The table on the Lookup Functions worksheet shows 25 recent sales. Each order lists the color of the boards ordered, the number to be shipped with the order, and the distance of the delivery in miles.
Complete the table by using the VLOOKUP and HLOOKUP functions in Excel to calculate the number of days it will take for delivery, the delivery price, and the price of the signs for each order.
3.1
Use the VLOOKUP function in cell F3 to determine the number of days it will take to ship order 1.
a. Use the distance in cell E3 to lookup the appropriate value on the "Delivery Information" reference table (range K4:M9).
b. Use relative and absolute references appropriately.
3.2
Copy your formula in cell F3 and paste it down to complete the "# Days" column of the table.
3.3
Use the VLOOKUP function in cell G3 to calculate the delivery price for order 1.
a. The delivery price can be referenced on the "Delivery Information" lookup table.
b. Use appropriate relative and absolute cell references.
3.4
Copy your formula in cell G3 and paste it down to complete the "Delivery" column of the table.
3.5
Use the HLOOKUP function in cell H3 to calculate the product pricing based on the color and quantity of the billboards ordered.
a. Product pricing can be referenced on the "Product Pricing" lookup table (range P3:Q12).
b. Use appropriate relative and absolute cell references.
c. Note the third argument within the HLOOKUP function is the row_index_num. It specifies how many rows to go down within the matching column to find the right value to return as the function result. For each Order, the value specified as '#'Shipped in Column D is the desired Row_index_num for that order (i.e., you should refer to the respective Column D cell for the third HLOOKUP argument). The row_index_num argument will be a reference to a cell in column D rather than a fixed number.
3.6
Copy your formula in cell H3 and paste it down to complete the "Price" column of the table.
4. Conditional Functions
The athletic department is sponsoring a free throw contest before tonight's game to give away free T-shirts. They have recorded the hair and eye color of each participant. Each participant shoots ten free throws. The hair and eye color groups with the highest average number of free throws made will get a T-shirt.
Complete the summary tables on the Conditional Functions worksheet to determine which groups made the most free throws. Then complete the table to determine which students get a shirt. How many students will get a T-shirt?
4.1
Use the COUNTIF function in cell I4 to determine the number of students with black hair. Be sure to build a formula that can be reused by copying down.
4.2
Copy your function in cell I4 and paste it down to complete the "Count" column of the "Hair Color Summary" table.
4.3
Use the COUNTIF function in cell I11 to determine the number of students with brown eyes. Be sure to build a formula that can be reused by copying down.
4.4
Copy your function in cell I11 and paste it down to complete the "Count" column of the "Eye Color Summary" table.
4.5
Use the SUMIF function in cell J4 to determine the total number of free throws made by students with black hair. Be sure to build a formula that can be reused by copying down.
4.6
Copy your function in cell J4 and paste it down to complete the "Sum" column of the "Hair Color Summary" table.
4.7
Use the SUMIF function in cell J11 to determine the total number of free throws made by students with brown eyes. Be sure to build a formula that can be reused by copying down.
4.8
Copy your function in cell J11 and paste it down to complete the "Sum" column of the "Eye Color Summary" table.
4.9
Use the AVERAGEIF function in cell K4 to determine the average number of free throws made by students with black hair. Be sure to build a formula that can be reused by copying down.
4.10
Copy your function in cell K4 and paste it down to complete the "Average" column of the "Hair Color Summary" table.
4.11
Use the AVERAGEIF function in cell K11 to determine the average number of free throws made by students with brown eyes. Be sure to build a formula that can be reused by copying down.
4.12
Copy your function in cell K11 and paste it down to complete the "Average" column of the "Eye Color Summary" table.
4.13
Use the OR function in cell F3 to determine if Student 1 gets a T-shirt. Students with "Red" as a hair color had the highest average number of free throws made on the "Hair Color Summary" table and students with Eye Color as "Hazel" had the highest average number of free throws made on the "Eye Color Summary" table.
a. Students get a T-shirt if their Hair Color is "Red" or their Eye Color is "Hazel".
4.14
Copy your formula in cell F3 and paste it down to complete the "Get Shirt?" column of the data table.
4.15
Use an appropriate function in cell K16 to calculate the number of T-shirts that will be given away.
Project 2.xlsx
IF Functions
| Price Per Donut | Commission Rates | Total Sales | $217.55 | |||||||
| 0 - 11 | $0.50 | Type | Threshold | Rate | Total Commission | $0.00 | ||||
| 12 | $0.40 | Beverage | 10% | |||||||
| Sales > $5 | $5.00 | 1% | Buy a donut? | |||||||
| Sales > $10 | $10.00 | 2% | Donut Threshold | $40.00 | ||||||
| Sales Amount | Commissions | |||||||||
| Order # | # Donuts | Beverage | Price/Donut | Donuts | Total | Rate | Sales | Beverage | Total Commission | |
| 1 | 60 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 2 | 12 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 3 | 48 | $66.24 | $0.00 | $66.24 | $0.00 | $0.00 | ||||
| 4 | 24 | $37.20 | $0.00 | $37.20 | $0.00 | $0.00 | ||||
| 5 | 10 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 6 | 3 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 7 | 6 | $7.50 | $0.00 | $7.50 | $0.00 | $0.00 | ||||
| 8 | 48 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 9 | 36 | $56.88 | $0.00 | $56.88 | $0.00 | $0.00 | ||||
| 10 | 6 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 11 | 4 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 12 | 3 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 13 | 9 | $11.61 | $0.00 | $11.61 | $0.00 | $0.00 | ||||
| 14 | 24 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 15 | 7 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 16 | 10 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 17 | 2 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 18 | 60 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 19 | 12 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 20 | 3 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 21 | 24 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 22 | 4 | $5.76 | $0.00 | $5.76 | $0.00 | $0.00 | ||||
| 23 | 24 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 24 | 8 | $10.40 | $0.00 | $10.40 | $0.00 | $0.00 | ||||
| 25 | 60 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 26 | 36 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 27 | 9 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 28 | 7 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 29 | 8 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 30 | 36 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 31 | 3 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 32 | 11 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 33 | 12 | $1.49 | $0.00 | $1.49 | $0.00 | $0.00 | ||||
| 34 | 3 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 35 | 60 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 36 | 36 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 37 | 36 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 38 | 36 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 39 | 48 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 40 | 9 | $14.31 | $0.00 | $14.31 | $0.00 | $0.00 | ||||
| 41 | 60 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 42 | 60 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 43 | 24 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 44 | 24 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 45 | 36 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 46 | 12 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 47 | 5 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 48 | 7 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 49 | 5 | $0.00 | $0.00 | $0.00 | $0.00 | $0.00 | ||||
| 50 | 4 | $6.16 | $0.00 | $6.16 | $0.00 | $0.00 |
Lookup Functions
| Order # | Color | # Shipped | Distance | # Days | Delivery | Price | Total | Delivery Information | Product Pricing | |||||||
| 1 | Red | 4 | 845 | $0 | Miles | # Days | Price | # Shipped | Blue | Red | ||||||
| 2 | Blue | 10 | 421 | $0 | 0 | 1 | $20.00 | 2 | $300 | $500 | ||||||
| 3 | Red | 5 | 5 | $0 | 50 | 2 | $30.00 | 3 | $400 | $700 | ||||||
| 4 | Red | 7 | 883 | $0 | 100 | 3 | $50.00 | 4 | $500 | $800 | ||||||
| 5 | Red | 4 | 181 | $0 | 200 | 5 | $75.00 | 5 | $600 | $975 | ||||||
| 6 | Blue | 9 | 117 | $0 | 400 | 7 | $150.00 | 6 | $700 | $1,100 | ||||||
| 7 | Red | 7 | 867 | $0 | 800 | 8 | $400.00 | 7 | $800 | $1,200 | ||||||
| 8 | Blue | 9 | 17 | $0 | 8 | $900 | $1,350 | |||||||||
| 9 | Blue | 6 | 646 | $0 | 9 | $1,000 | $1,425 | |||||||||
| 10 | Blue | 2 | 486 | $0 | 10 | $1,100 | $1,550 | |||||||||
| 11 | Red | 9 | 195 | $0 | ||||||||||||
| 12 | Blue | 6 | 374 | $0 | ||||||||||||
| 13 | Blue | 4 | 74 | $0 | ||||||||||||
| 14 | Blue | 7 | 17 | $0 | ||||||||||||
| 15 | Blue | 6 | 822 | $0 | ||||||||||||
| 16 | Blue | 9 | 940 | $0 | ||||||||||||
| 17 | Red | 4 | 140 | $0 | ||||||||||||
| 18 | Blue | 3 | 56 | $0 | ||||||||||||
| 19 | Red | 10 | 823 | $0 | ||||||||||||
| 20 | Blue | 2 | 705 | $0 | ||||||||||||
| 21 | Red | 7 | 54 | $0 | ||||||||||||
| 22 | Red | 3 | 35 | $0 | ||||||||||||
| 23 | Red | 4 | 684 | $0 | ||||||||||||
| 24 | Red | 9 | 60 | $0 | ||||||||||||
| 25 | Red | 6 | 86 | $0 | ||||||||||||
Conditional Functions
| Student | Hair Color | Eye Color | Free Throws | Get Shirt? | Hair Color Summary | |||||
| 1 | Brown | Green | 3 | Count | Sum | Average | ||||
| 2 | Brown | Brown | 4 | Black | ||||||
| 3 | Brown | Brown | 9 | Brown | ||||||
| 4 | Black | Brown | 9 | Red | ||||||
| 5 | Brown | Blue | 1 | Blond | ||||||
| 6 | Black | Blue | 7 | |||||||
| 7 | Brown | Blue | 8 | Eye Color Summary | ||||||
| 8 | Brown | Blue | 7 | Count | Sum | Average | ||||
| 9 | Black | Brown | 2 | Brown | ||||||
| 10 | Red | Blue | 8 | Blue | ||||||
| 11 | Brown | Brown | 9 | Hazel | ||||||
| 12 | Black | Brown | 6 | Green | ||||||
| 13 | Brown | Green | 10 | |||||||
| 14 | Black | Brown | 8 | How many students will get a shirt? | ||||||
| 15 | Brown | Blue | 0 | |||||||
| 16 | Brown | Hazel | 6 | |||||||
| 17 | Black | Brown | 4 | |||||||
| 18 | Black | Blue | 4 | |||||||
| 19 | Brown | Blue | 6 | |||||||
| 20 | Blond | Blue | 8 | |||||||
| 21 | Black | Brown | 2 | |||||||
| 22 | Brown | Brown | 7 | |||||||
| 23 | Blond | Blue | 4 | |||||||
| 24 | Red | Hazel | 1 | |||||||
| 25 | Blond | Blue | 5 | |||||||
| 26 | Black | Green | 4 | |||||||
| 27 | Black | Brown | 9 | |||||||
| 28 | Brown | Hazel | 4 | |||||||
| 29 | Black | Brown | 5 | |||||||
| 30 | Black | Blue | 10 | |||||||
| 31 | Brown | Green | 8 | |||||||
| 32 | Brown | Hazel | 6 | |||||||
| 33 | Blond | Blue | 10 | |||||||
| 34 | Brown | Brown | 0 | |||||||
| 35 | Black | Brown | 10 | |||||||
| 36 | Brown | Brown | 2 | |||||||
| 37 | Black | Green | 8 | |||||||
| 38 | Brown | Brown | 3 | |||||||
| 39 | Brown | Blue | 0 | |||||||
| 40 | Brown | Hazel | 8 | |||||||
| 41 | Black | Green | 10 | |||||||
| 42 | Red | Blue | 0 | |||||||
| 43 | Brown | Blue | 4 | |||||||
| 44 | Brown | Blue | 0 | |||||||
| 45 | Black | Hazel | 1 | |||||||
| 46 | Red | Hazel | 5 | |||||||
| 47 | Red | Green | 8 | |||||||
| 48 | Brown | Brown | 5 | |||||||
| 49 | Red | Brown | 9 | |||||||
| 50 | Brown | Brown | 5 | |||||||
| 51 | Blond | Green | 6 | |||||||
| 52 | Red | Green | 8 | |||||||
| 53 | Brown | Blue | 9 | |||||||
| 54 | Brown | Hazel | 0 | |||||||
| 55 | Brown | Brown | 1 | |||||||
| 56 | Red | Green | 5 | |||||||
| 57 | Red | Brown | 9 | |||||||
| 58 | Brown | Brown | 3 | |||||||
| 59 | Brown | Brown | 5 | |||||||
| 60 | Brown | Brown | 10 | |||||||
| 61 | Blond | Blue | 8 | |||||||
| 62 | Brown | Hazel | 9 | |||||||
| 63 | Black | Green | 2 | |||||||
| 64 | Brown | Green | 7 | |||||||
| 65 | Brown | Brown | 3 | |||||||
| 66 | Black | Brown | 6 | |||||||
| 67 | Blond | Green | 9 | |||||||
| 68 | Blond | Blue | 4 | |||||||
| 69 | Red | Blue | 8 | |||||||
| 70 | Brown | Blue | 6 | |||||||
| 71 | Black | Blue | 1 | |||||||
| 72 | Blond | Brown | 5 | |||||||
| 73 | Brown | Green | 9 | |||||||
| 74 | Brown | Hazel | 4 | |||||||
| 75 | Brown | Brown | 6 | |||||||
| 76 | Brown | Hazel | 0 | |||||||
| 77 | Red | Green | 10 | |||||||
| 78 | Brown | Hazel | 4 | |||||||
| 79 | Brown | Blue | 3 | |||||||
| 80 | Blond | Blue | 8 | |||||||
| 81 | Blond | Blue | 6 | |||||||
| 82 | Black | Brown | 8 | |||||||
| 83 | Black | Hazel | 10 | |||||||
| 84 | Brown | Brown | 9 | |||||||
| 85 | Blond | Blue | 6 | |||||||
| 86 | Blond | Blue | 3 | |||||||
| 87 | Brown | Brown | 0 | |||||||
| 88 | Brown | Green | 7 | |||||||
| 89 | Red | Brown | 9 | |||||||
| 90 | Red | Blue | 10 | |||||||
| 91 | Blond | Green | 4 | |||||||
| 92 | Brown | Brown | 3 | |||||||
| 93 | Brown | Blue | 4 | |||||||
| 94 | Brown | Green | 3 | |||||||
| 95 | Brown | Blue | 7 | |||||||
| 96 | Black | Brown | 10 | |||||||
| 97 | Brown | Brown | 10 | |||||||
| 98 | Brown | Green | 3 | |||||||
| 99 | Red | Green | 7 | |||||||
| 100 | Black | Brown | 2 | |||||||
| 101 | Brown | Blue | 4 | |||||||
| 102 | Brown | Brown | 6 | |||||||
| 103 | Black | Brown | 1 | |||||||
| 104 | Black | Brown | 4 | |||||||
| 105 | Brown | Brown | 0 | |||||||
| 106 | Brown | Brown | 2 | |||||||
| 107 | Brown | Green | 4 | |||||||
| 108 | Brown | Brown | 6 | |||||||
| 109 | Brown | Brown | 8 | |||||||
| 110 | Brown | Brown | 3 | |||||||
| 111 | Brown | Blue | 9 | |||||||
| 112 | Red | Green | 8 | |||||||
| 113 | Brown | Brown | 6 | |||||||
| 114 | Blond | Blue | 5 | |||||||
| 115 | Brown | Green | 8 | |||||||
| 116 | Blond | Blue | 2 | |||||||
| 117 | Black | Brown | 5 | |||||||
| 118 | Brown | Brown | 0 | |||||||
| 119 | Brown | Blue | 0 | |||||||
| 120 | Blond | Blue | 1 | |||||||
| 121 | Blond | Blue | 2 | |||||||
| 122 | Blond | Blue | 8 | |||||||
| 123 | Brown | Hazel | 6 | |||||||
| 124 | Black | Brown | 7 | |||||||
| 125 | Brown | Green | 4 | |||||||
| 126 | Brown | Blue | 4 | |||||||
| 127 | Brown | Brown | 9 | |||||||
| 128 | Brown | Hazel | 8 | |||||||
| 129 | Brown | Brown | 3 | |||||||
| 130 | Black | Brown | 1 | |||||||
| 131 | Brown | Blue | 9 | |||||||
| 132 | Brown | Blue | 9 | |||||||
| 133 | Blond | Green | 0 | |||||||
| 134 | Blond | Blue | 5 | |||||||
| 135 | Brown | Blue | 3 | |||||||
| 136 | Red | Brown | 2 | |||||||
| 137 | Brown | Brown | 1 | |||||||
| 138 | Blond | Blue | 7 | |||||||
| 139 | Black | Hazel | 7 | |||||||
| 140 | Brown | Blue | 7 | |||||||
| 141 | Brown | Blue | 5 | |||||||
| 142 | Brown | Blue | 2 | |||||||
| 143 | Blond | Blue | 7 | |||||||
| 144 | Red | Brown | 6 | |||||||
| 145 | Brown | Hazel | 8 | |||||||
| 146 | Brown | Blue | 2 | |||||||
| 147 | Black | Hazel | 5 | |||||||
| 148 | Brown | Brown | 7 | |||||||
| 149 | Brown | Hazel | 5 | |||||||
| 150 | Brown | Hazel | 5 | |||||||
| 151 | Blond | Blue | 4 | |||||||
| 152 | Brown | Blue | 6 | |||||||
| 153 | Blond | Blue | 2 | |||||||
| 154 | Black | Brown | 3 | |||||||
| 155 | Brown | Blue | 5 | |||||||
| 156 | Blond | Brown | 9 | |||||||
| 157 | Blond | Blue | 10 | |||||||
| 158 | Blond | Green | 2 | |||||||
| 159 | Brown | Hazel | 6 | |||||||
| 160 | Brown | Blue | 4 | |||||||
| 161 | Brown | Blue | 0 | |||||||
| 162 | Brown | Hazel | 1 | |||||||
| 163 | Brown | Blue | 9 | |||||||
| 164 | Black | Brown | 5 | |||||||
| 165 | Brown | Brown | 5 | |||||||
| 166 | Blond | Blue | 10 | |||||||
| 167 | Blond | Blue | 4 | |||||||
| 168 | Black | Brown | 9 | |||||||
| 169 | Blond | Blue | 4 | |||||||
| 170 | Brown | Brown | 10 | |||||||
| 171 | Black | Blue | 1 | |||||||
| 172 | Blond | Blue | 8 | |||||||
| 173 | Blond | Blue | 1 | |||||||
| 174 | Red | Brown | 8 | |||||||
| 175 | Brown | Brown | 9 | |||||||
| 176 | Brown | Brown | 2 | |||||||
| 177 | Black | Hazel | 9 | |||||||
| 178 | Brown | Brown | 5 | |||||||
| 179 | Blond | Blue | 7 | |||||||
| 180 | Brown | Brown | 2 | |||||||
| 181 | Brown | Green | 7 | |||||||
| 182 | Brown | Hazel | 8 | |||||||
| 183 | Red | Blue | 1 | |||||||
| 184 | Brown | Brown | 7 | |||||||
| 185 | Blond | Green | 7 | |||||||
| 186 | Red | Green | 9 | |||||||
| 187 | Brown | Green | 4 | |||||||
| 188 | Brown | Brown | 2 | |||||||
| 189 | Blond | Hazel | 5 | |||||||
| 190 | Brown | Brown | 2 | |||||||
| 191 | Brown | Brown | 6 | |||||||
| 192 | Brown | Brown | 1 | |||||||
| 193 | Brown | Blue | 2 | |||||||
| 194 | Brown | Brown | 7 | |||||||
| 195 | Black | Brown | 4 | |||||||
| 196 | Blond | Blue | 3 | |||||||
| 197 | Brown | Green | 5 | |||||||
| 198 | Blond | Blue | 0 | |||||||
| 199 | Blond | Blue | 6 | |||||||
| 200 | Brown | Brown | 10 | |||||||
| 201 | Blond | Blue | 3 | |||||||
| 202 | Red | Brown | 6 | |||||||
| 203 | Brown | Green | 6 | |||||||
| 204 | Brown | Brown | 2 | |||||||
| 205 | Red | Brown | 7 | |||||||
| 206 | Brown | Blue | 0 | |||||||
| 207 | Red | Green | 6 | |||||||
| 208 | Red | Blue | 10 | |||||||
| 209 | Brown | Blue | 6 | |||||||
| 210 | Red | Blue | 8 | |||||||
| 211 | Blond | Green | 2 | |||||||
| 212 | Blond | Blue | 1 | |||||||
| 213 | Black | Blue | 8 | |||||||
| 214 | Blond | Blue | 0 | |||||||
| 215 | Red | Hazel | 10 | |||||||
| 216 | Blond | Blue | 10 | |||||||
| 217 | Red | Green | 3 | |||||||
| 218 | Black | Brown | 6 | |||||||
| 219 | Brown | Green | 3 | |||||||
| 220 | Blond | Green | 0 | |||||||
| 221 | Blond | Blue | 1 | |||||||
| 222 | Brown | Blue | 2 | |||||||
| 223 | Brown | Green | 1 | |||||||
| 224 | Brown | Green | 7 | |||||||
| 225 | Brown | Brown | 8 | |||||||
| 226 | Blond | Blue | 3 | |||||||
| 227 | Red | Hazel | 10 | |||||||
| 228 | Black | Brown | 2 | |||||||
| 229 | Red | Green | 2 | |||||||
| 230 | Blond | Blue | 1 | |||||||
| 231 | Black | Brown | 3 | |||||||
| 232 | Blond | Hazel | 3 | |||||||
| 233 | Red | Hazel | 2 | |||||||
| 234 | Brown | Brown | 5 | |||||||
| 235 | Blond | Blue | 8 | |||||||
| 236 | Black | Brown | 5 | |||||||
| 237 | Blond | Hazel | 6 | |||||||
| 238 | Brown | Brown | 9 | |||||||
| 239 | Brown | Blue | 6 | |||||||
| 240 | Blond | Blue | 8 | |||||||
| 241 | Brown | Hazel | 3 | |||||||
| 242 | Brown | Brown | 10 | |||||||
| 243 | Blond | Blue | 9 | |||||||
| 244 | Brown | Hazel | 6 | |||||||
| 245 | Blond | Green | 6 | |||||||
| 246 | Blond | Brown | 4 | |||||||
| 247 | Blond | Hazel | 3 | |||||||
| 248 | Brown | Brown | 8 | |||||||
| 249 | Brown | Brown | 5 | |||||||
| 250 | Blond | Blue | 2 | |||||||
| 251 | Brown | Hazel | 4 | |||||||
| 252 | Brown | Hazel | 10 | |||||||
| 253 | Brown | Green | 10 | |||||||
| 254 | Black | Hazel | 4 | |||||||
| 255 | Black | Brown | 2 | |||||||
| 256 | Brown | Hazel | 9 | |||||||
| 257 | Black | Hazel | 3 | |||||||
| 258 | Blond | Green | 1 | |||||||
| 259 | Brown | Green | 5 | |||||||
| 260 | Brown | Hazel | 2 | |||||||
| 261 | Brown | Blue | 8 | |||||||
| 262 | Blond | Blue | 5 | |||||||
| 263 | Blond | Hazel | 9 | |||||||
| 264 | Brown | Blue | 2 | |||||||
| 265 | Black | Brown | 10 | |||||||
| 266 | Black | Blue | 9 | |||||||
| 267 | Blond | Brown | 2 | |||||||
| 268 | Brown | Hazel | 10 | |||||||
| 269 | Brown | Green | 4 | |||||||
| 270 | Black | Brown | 9 | |||||||
| 271 | Brown | Blue | 5 | |||||||
| 272 | Brown | Hazel | 10 | |||||||
| 273 | Brown | Hazel | 10 | |||||||
| 274 | Blond | Blue | 9 | |||||||
| 275 | Brown | Hazel | 4 | |||||||
| 276 | Brown | Blue | 1 | |||||||
| 277 | Black | Brown | 3 | |||||||
| 278 | Red | Blue | 5 | |||||||
| 279 | Brown | Hazel | 8 | |||||||
| 280 | Blond | Hazel | 9 | |||||||
| 281 | Brown | Blue | 10 | |||||||
| 282 | Brown | Brown | 10 | |||||||
| 283 | Red | Green | 8 | |||||||
| 284 | Black | Brown | 0 | |||||||
| 285 | Blond | Blue | 8 | |||||||
| 286 | Black | Brown | 9 | |||||||
| 287 | Brown | Brown | 6 | |||||||
| 288 | Brown | Blue | 2 | |||||||
| 289 | Brown | Brown | 4 | |||||||
| 290 | Brown | Hazel | 1 | |||||||
| 291 | Blond | Blue | 3 | |||||||
| 292 | Blond | Blue | 7 | |||||||
| 293 | Blond | Blue | 1 | |||||||
| 294 | Brown | Brown | 4 | |||||||
| 295 | Blond | Hazel | 1 | |||||||
| 296 | Black | Hazel | 8 | |||||||
| 297 | Blond | Blue | 4 | |||||||
| 298 | Black | Brown | 8 | |||||||
| 299 | Brown | Brown | 10 | |||||||
| 300 | Blond | Blue | 6 |
Boolean Functions
| Scenario | Runner on 1st | Runner on 2nd | Catchable Fly Ball Hit to... | Outs | Force at Third | Fly Ball | Not 2 Outs | Infield Fly | ||
| Infield | Shallow Outfield | |||||||||
| 1 | Yes | No | No | Yes | 0 | |||||
| 2 | Yes | No | Yes | No | 0 | |||||
| 3 | Yes | No | No | Yes | 0 | |||||
| 4 | No | Yes | No | No | 2 | |||||
| 5 | Yes | Yes | No | No | 0 | |||||
| 6 | No | No | No | Yes | 1 | |||||
| 7 | Yes | Yes | No | No | 1 | |||||
| 8 | Yes | Yes | No | Yes | 2 | |||||
| 9 | Yes | Yes | No | No | 1 | |||||
| 10 | Yes | Yes | No | Yes | 1 | |||||
| 11 | No | No | No | No | 0 | |||||
| 12 | Yes | Yes | Yes | No | 2 | |||||
| 13 | No | Yes | No | Yes | 0 | |||||
| 14 | No | Yes | No | No | 2 | |||||
| 15 | Yes | Yes | Yes | No | 1 | |||||
| 16 | No | No | Yes | No | 0 | |||||
| 17 | No | Yes | Yes | No | 0 | |||||
| 18 | No | No | Yes | No | 0 | |||||
| 19 | No | No | No | No | 0 | |||||
| 20 | Yes | Yes | No | Yes | 0 | |||||
| 21 | Yes | Yes | Yes | No | 0 | |||||
| 22 | No | No | Yes | No | 1 | |||||
| 23 | No | Yes | Yes | No | 1 | |||||
| 24 | No | Yes | No | Yes | 0 | |||||
| 25 | No | No | No | Yes | 0 | |||||
| 26 | Yes | No | Yes | No | 1 | |||||
| 27 | Yes | Yes | No | Yes | 0 | |||||
| 28 | No | No | No | Yes | 1 | |||||
| 29 | No | No | No | Yes | 2 | |||||
| 30 | No | No | No | Yes | 2 |
Project 3 instructions.docx
1. Securities and Exchange Commission
Use date and time functions to complete the tasks.
1.1
Use the TODAY function to insert the current date in cell C6.
1.2
Use the NOW function to insert the current time in cell C8.
1.3
Use the MONTH function in cell C10 to calculate the month of the year for the date of the formation of the SEC, entered in cell C4.
1.4
Use the YEAR function in cell C12 to calculate the year for the date of the formation of the SEC, entered in cell C4.
1.5
Calculate the difference between the date of the formation of the SEC, entered in cell C4, and the current date in cell C6. Insert this calculation in cell C14.
1.6
Calculate the number of hours since the date of the formation of the SEC. Insert your calculation in cell C15.
Hint: convert the number of days to number of hours by multiplying the number of days in C14 by 24.
1.7
Calculate the number of minutes since the date of the formation of the SEC. Insert your calculation in cell C16. Be sure to reference the number of hours calculated in the previous task in your calculation.
1.8
Calculate the number of seconds since the date of the formation of the SEC. Insert your calculation in cell C17. Be sure to reference the number of minutes calculated in the previous task in your calculation.
2. Security
You are responsible for monitoring employees' entry into your company's server room. To complete this task, you need to review a log of when employees entered the room. The log is a little bit difficult to read. It contains:
· An employee number (column B).
· A computer generated stamp that records when the employee swiped their ID card to enter the room (column C).
Ultimately, you want to create the "Text Stamp" in column K that completes the phrase, "Employee number # entered the server room at HH:MM:SS today." for each row of the log.
You will replace "#" with the employee number and "HH:MM:SS" with the appropriate time stamp.
Use time and text functions to complete the tasks below.
2.1
Use the LEN function in cell C3 to calculate the length of the "Text Stamp Phrase" in cell C2.
2.2
Use the SEARCH function in cell C4 to determine the position of the "#" symbol in the "Text Stamp Phrase" in cell C2.
2.3
Use the LEFT function in cell C5 to return the text "Employee number " from the "Text Stamp Phrase" in cell C2. Notice the space after number.
a. Use a reference to the location of the "#" symbol in cell C4 as the "[num_chars]" argument.
b. Since the "#" symbol is one character past the text you want to return, you will need to adjust the "[num_chars]" argument by subtracting 1 from the reference to cell C4.
2.4
Use the SEARCH function in cell C6 to determine the position of the characters " HH" (notice the space before the first H) in the "Text Stamp Phrase" in cell C2.
2.5
Use the MID function in cell C7 to return the text " entered the server room at " (notice the spaces at the beginning and end of the phrase) from the "Text Stamp Phrase" in cell C2.
a. Use a reference to the location of the "#" symbol in cell C4 as the "start_num" argument.
b. Since the "#" symbol is 1 character before the text you want to return, you will need to adjust the "[num_chars]" argument by adding 1 to the reference to cell C4.
c. Use the difference between the location of the "#" symbol (cell C4) and the characters " HH" (cell C6) as the "num_chars" argument.
2.6
Use the RIGHT function in cell C8 to return the text " today." from the end of the "Text Stamp Phrase" in cell C2.
The "[num_chars]" argument for your function is 7 since there are seven characters in the text " today." (including the space at the beginning).
2.7
Use the HOUR function in cell D12 to calculate the "Hour" portion of the "Entry Swipe" found in cell C12. Copy and paste the function down to complete the "Hour" column of the table.
2.8
Use the MINUTE function in cell E12 to calculate the "Minute" portion of the "Entry Swipe" found in cell C12. Copy and paste the function down to complete the "Minute" column of the table.
2.9
Use the SECOND function in cell F12 to calculate the "Second" portion of the "Entry Swipe" found in cell C12. Copy and paste the function down to complete the "Second" column of the table.
2.10
Use the CONCAT function in cell J12 to combine the text in cells G12, H12, and I12 to create a "Time Stamp".
a. Notice that the syntax for the "Time Stamp" is "HH:MM:SS".
b. You will need to insert the ":" symbol between "Hour" and "Minute" and between "Minute" and "Second".
c. Hint: you should have five arguments for your function. arguments 2 and 4 should be the ":" symbol.
d. Copy and paste your function to complete the "Time Stamp" column of the table.
Project 3.xlsx
Security
| Text Stamp Phrase: | Employee number # entered the server room at HH:MM:SS today. | |||||||||
| Phrase length: | ||||||||||
| Location of "#" | ||||||||||
| Phrase Part 1 | ||||||||||
| Location of " HH" | ||||||||||
| Phrase Part 2 | ||||||||||
| Phrase Part 3 | ||||||||||
| Employee | Entry Swipe | Hour | Minute | Second | 2 Decimal Equivalents | Time Stamp | Text Stamp | |||
| Hour | Minute | Second | ||||||||
| 18 | 0.03683 | 00 | 00 | 00 | ||||||
| 43 | 0.07735 | 00 | 00 | 00 | ||||||
| 47 | 0.07851 | 00 | 00 | 00 | ||||||
| 33 | 0.11561 | 00 | 00 | 00 | ||||||
| 9 | 0.12751 | 00 | 00 | 00 | ||||||
| 40 | 0.13393 | 00 | 00 | 00 | ||||||
| 31 | 0.15009 | 00 | 00 | 00 | ||||||
| 35 | 0.15765 | 00 | 00 | 00 | ||||||
| 34 | 0.1589 | 00 | 00 | 00 | ||||||
| 7 | 0.16495 | 00 | 00 | 00 | ||||||
| 14 | 0.17773 | 00 | 00 | 00 | ||||||
| 32 | 0.19499 | 00 | 00 | 00 | ||||||
| 26 | 0.20724 | 00 | 00 | 00 | ||||||
| 31 | 0.22585 | 00 | 00 | 00 | ||||||
| 2 | 0.23963 | 00 | 00 | 00 | ||||||
| 49 | 0.26752 | 00 | 00 | 00 | ||||||
| 1 | 0.27961 | 00 | 00 | 00 | ||||||
| 23 | 0.29132 | 00 | 00 | 00 | ||||||
| 3 | 0.34089 | 00 | 00 | 00 | ||||||
| 5 | 0.34756 | 00 | 00 | 00 | ||||||
| 43 | 0.36121 | 00 | 00 | 00 | ||||||
| 49 | 0.47293 | 00 | 00 | 00 | ||||||
| 34 | 0.47747 | 00 | 00 | 00 | ||||||
| 43 | 0.50246 | 00 | 00 | 00 | ||||||
| 20 | 0.50331 | 00 | 00 | 00 | ||||||
| 12 | 0.51943 | 00 | 00 | 00 | ||||||
| 26 | 0.53676 | 00 | 00 | 00 | ||||||
| 10 | 0.53725 | 00 | 00 | 00 | ||||||
| 44 | 0.53871 | 00 | 00 | 00 | ||||||
| 29 | 0.55506 | 00 | 00 | 00 | ||||||
| 28 | 0.58649 | 00 | 00 | 00 | ||||||
| 11 | 0.61908 | 00 | 00 | 00 | ||||||
| 47 | 0.62841 | 00 | 00 | 00 | ||||||
| 18 | 0.63078 | 00 | 00 | 00 | ||||||
| 18 | 0.63232 | 00 | 00 | 00 | ||||||
| 21 | 0.657 | 00 | 00 | 00 | ||||||
| 9 | 0.6789 | 00 | 00 | 00 | ||||||
| 10 | 0.74752 | 00 | 00 | 00 | ||||||
| 47 | 0.74839 | 00 | 00 | 00 | ||||||
| 43 | 0.75177 | 00 | 00 | 00 | ||||||
| 42 | 0.76064 | 00 | 00 | 00 | ||||||
| 8 | 0.7803 | 00 | 00 | 00 | ||||||
| 8 | 0.78321 | 00 | 00 | 00 | ||||||
| 21 | 0.83912 | 00 | 00 | 00 | ||||||
| 24 | 0.86551 | 00 | 00 | 00 | ||||||
| 8 | 0.8737 | 00 | 00 | 00 | ||||||
| 6 | 0.88209 | 00 | 00 | 00 | ||||||
| 5 | 0.88355 | 00 | 00 | 00 | ||||||
| 12 | 0.89924 | 00 | 00 | 00 | ||||||
| 32 | 0.92394 | 00 | 00 | 00 | ||||||
| 46 | 0.92571 | 00 | 00 | 00 | ||||||
| 10 | 0.93176 | 00 | 00 | 00 | ||||||
| 46 | 0.96428 | 00 | 00 | 00 | ||||||
| 24 | 0.96981 | 00 | 00 | 00 | ||||||
| 1 | 0.97889 | 00 | 00 | 00 |
SEC
| Date and Time Functions | |||
| Securities and Exchange Commission formed on | 6/6/34 | ||
| Today's Date | |||
| The current time | |||
| The month the SEC was formed | |||
| The year the SEC was formed | |||
| Time since the formation of the SEC | days | ||
| hours | |||
| minutes | |||
| seconds |
Project 4 instructions.docx
1. Education
The Education table contains information about the median annual income and 2018 unemployment rate for Americans with various levels of education. Use charts to analyze the relationship between education and income as well as education and unemployment.
1.1
Construct a clustered column chart to examine the median annual earnings for each level of education.
a. Select the range for each education level (B3:B10) and the values for median income (C3:C10) before inserting the chart.
b. Format the chart with the title "Education and Income" at the top.
c. Show each education level on the horizontal axis. Note: if you have selected the education levels and income data before inserting the chart, this will likely already be the case.
d. Do not display a legend.
e. Show data labels on the top of each of the "columns" of the graph.
f. Place the top left corner of the chart over the specified range of the worksheet.
1.2
Construct a clustered column chart to examine the unemployment rate for each level of education.
a. Select the range for each education level (B3:B10) and the values for the unemployment rate (D3:D10) before inserting the chart.
b. Format the chart with the title "Education and Unemployment" at the top.
c. Display each education level on the horizontal axis. Note: if you have selected the education levels and unemployment data before inserting the chart, this will likely already be the case.
d. Do not show the legend.
e. Show data labels on the top of each of the "columns" of the graph.
f. Place the top left corner of the chart over the specified range of the worksheet.
2. Apple
The "Apple Revenue" table details the revenue for Apple in each quarter from 2014-2018.
2.1
Construct a stacked column chart to compare the revenue totals for each year.
a. Select the range for the years, the four quarters, and the sales data in each of the quarters (B3:G7)
b. Do not include the total revenue range in the chart (B8:G8).
c. Format the chart with the title "Apple Revenue by Quarter".
d. Use the year as the horizontal axis. Note: this will likely already be the case if you have selected the correct range before inserting the chart.
e. Display the quarterly revenues "stacked" in each column.
f. Add a legend that depicts each quarter.
g. Place the top left of the chart over the specified range in the worksheet.
3. PC Shipments
The growth in Worldwide PC shipments was driven by demand created by the Windows 10 refresh in the business market. The table on the PC Shipments worksheet details shipments from several PC manufacturers (in thousands) to PC retailers and customers in the second quarters of 2018 and 2019.
3.1
Construct a pie chart to compare the shipment totals for each company (and "Others") in the second quarter of 2019.
a. Select the range for each company (B3:B8) and the 2019 sales data (C3:C8) before inserting the chart.
b. Format the chart with the title "PC Shipments 2Q 2019".
c. Display a legend that depicts each company. Note: this will likely already be the case if you have selected an appropriate range before inserting the chart.
d. Include data labels with the percentage for each company on the chart.
e. Place the top left of the chart over the specified range in the worksheet.
3.2
Construct a pie chart to compare the shipment totals for each company (and "Others") in the second quarter of 2018.
a. Select the range for each company (B3:B8) and the 2018 sales data (D3:D8) before inserting the chart.
b. Format the chart with the title "PC Shipments 2Q 2018".
c. Display a legend that depicts each company. Note: this will likely already be the case if you have selected an appropriate range before inserting the chart.
d. Add data labels with the percentage for each company on the chart.
e. Place the top left of the chart over the specified range in the worksheet.
4. Utah Population
The population of Utah is growing rapidly. Net migration contributed 33% of Utah's population growth since 2010. The Utah Population table details the estimated population for each county in Utah for each year from 2010 to 2019.
Complete the tasks to look for trends or patterns in the population growth in the state.
4.1
Construct a line chart to examine the "State Total" growth (row 11 of the worksheet) for the years 2010-2019.
a. Select the range for the years and the state population totals (B10:L11) before inserting the chart.
b. Format the chart with the title "Total Utah Population".
c. Display a legend to the right of the chart.
d. Use the dates in row 10 of the worksheet as the labels for the horizontal axis. Note: this will likely already be the case if you select an appropriate range before inserting the chart.
e. Place the top left corner of the chart over the specified range in the worksheet.
4.2
Construct a line chart to compare the population growth for the years 2010-2019 in the following counties: Davis, Salt Lake, Utah, and Weber.
a. Notice the smaller data table (range B3:L7) to be used for creating this chart. Cells B4:B7 contain drop-down lists that can be used to easily change the county population data presented in the table.
b. Select this range before inserting the chart.
c. Format the chart with the title "Population in Selected Counties".
d. Display a legend.
e. Use the dates in row 2 of the worksheet as the labels for the horizontal axis. Note: this will likely already be the case if an appropriate range is selected before inserting the chart.
f. Place the top left corner of the chart over the specified range in the worksheet.
Project 4.xlsx
Apple
| Revenues in billions of dollars | ||||||
| Quarter | 2014 | 2015 | 2016 | 2017 | 2018 | |
| Q1 | $37.47 | $74.60 | $75.87 | $78.35 | $88.29 | |
| Q2 | $45.65 | $58.01 | $50.56 | $52.90 | $61.14 | |
| Q3 | $37.43 | $49.61 | $42.36 | $45.41 | $53.27 | |
| Q4 | $42.12 | $51.50 | $46.85 | $52.58 | $62.90 | |
| Total | $162.67 | $233.72 | $215.64 | $229.24 | $265.60 | |
| Place the "Apple Revenue" Chart Here for Grading | ||||||
PC Shipments
| Company | 2Q19 Shipments | 2Q18 Shipments | Place the "PC Shipments 2Q 2018" Chart Here for Grading | ||
| Lenovo | 16,254 | 13,750 | |||
| HP Inc. | 15,356 | 14,880 | |||
| Dell | 11,606 | 11,255 | |||
| Apple | 4,288 | 4,363 | |||
| Acer Group | 4,077 | 3,722 | |||
| Others | 13,276 | 13,961 | |||
| Place the "PC Shipments 2Q 2019" Chart Here for Grading | |||||
Utah Population
| Population Data for the Population in Selected Counties Chart | |||||||||||||
| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | Place the "Total Utah Population" Chart Here for Grading | |||
| Davis County | 307,625 | 313,280 | 318,477 | 324,410 | 329,842 | 336,106 | 342,658 | 348,763 | 352,805 | 356,964 | |||
| Salt Lake County | 1,031,697 | 1,046,461 | 1,060,336 | 1,070,815 | 1,080,905 | 1,094,681 | 1,108,910 | 1,128,271 | 1,142,081 | 1,152,960 | |||
| Utah County | 518,872 | 532,753 | 544,892 | 554,405 | 567,218 | 585,719 | 603,385 | 617,735 | 633,582 | 651,409 | |||
| Weber County | 231,833 | 233,819 | 236,391 | 237,921 | 239,588 | 242,753 | 245,687 | 248,835 | 251,571 | 253,455 | |||
| Complete Population Data | |||||||||||||
| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | ||||
| State Total | 2,772,371 | 2,820,613 | 2,864,744 | 2,902,179 | 2,941,964 | 2,997,584 | 3,054,994 | 3,113,983 | 3,166,666 | 3,220,262 | |||
| Beaver County | 6,643 | 6,658 | 6,670 | 6,754 | 6,661 | 6,710 | 6,782 | 6,843 | 6,910 | 6,976 | |||
| Box Elder County | 50,067 | 50,640 | 51,155 | 51,795 | 52,282 | 52,971 | 54,040 | 54,971 | 55,685 | 56,329 | |||
| Cache County | 113,307 | 115,004 | 116,404 | 117,600 | 118,876 | 121,873 | 123,926 | 126,490 | 128,887 | 131,387 | |||
| Carbon County | 21,419 | 21,505 | 21,590 | 21,341 | 21,203 | 21,168 | 21,193 | 21,209 | 21,396 | 21,482 | |||
| Daggett County | 1,078 | 1,109 | 1,114 | 1,157 | 1,113 | 1,114 | 1,104 | 1,052 | 1,061 | 1,073 | |||
| Davis County | 307,625 | 313,280 | 318,477 | 324,410 | 329,842 | 336,106 | 342,658 | 348,763 | 352,805 | 356,964 | |||
| Duchesne County | 18,721 | 19,020 | 19,696 | 20,283 | 20,577 | 20,822 | 20,609 | 20,828 | 20,850 | 20,846 | |||
| Emery County | 11,012 | 11,128 | 10,964 | 10,945 | 10,845 | 10,662 | 10,577 | 10,672 | 10,669 | 10,666 | |||
| Garfield County | 5,171 | 5,203 | 5,226 | 5,220 | 5,194 | 5,164 | 5,191 | 5,240 | 5,229 | 5,226 | |||
| Grand County | 9,238 | 9,395 | 9,529 | 9,553 | 9,631 | 9,764 | 9,943 | 10,059 | 10,262 | 10,117 | Place the "Population in Selected Counties" Chart Here for Grading | ||
| Iron County | 46,221 | 46,955 | 47,311 | 47,622 | 48,193 | 49,412 | 50,747 | 52,278 | 54,151 | 55,401 | |||
| Juab County | 10,280 | 10,380 | 10,485 | 10,604 | 10,824 | 11,072 | 11,542 | 11,798 | 12,177 | 12,455 | |||
| Kane County | 7,116 | 7,200 | 7,302 | 7,321 | 7,268 | 7,272 | 7,583 | 7,558 | 7,718 | 7,716 | |||
| Millard County | 12,535 | 12,706 | 12,816 | 12,956 | 13,023 | 13,105 | 13,291 | 13,477 | 13,586 | 13,743 | |||
| Morgan County | 9,518 | 9,714 | 10,049 | 10,418 | 10,776 | 11,081 | 11,522 | 11,725 | 11,963 | 12,189 | |||
| Piute County | 1,555 | 1,576 | 1,585 | 1,603 | 1,594 | 1,632 | 1,604 | 1,607 | 1,663 | 1,711 | |||
| Rich County | 2,278 | 2,291 | 2,277 | 2,300 | 2,324 | 2,355 | 2,357 | 2,371 | 2,428 | 2,398 | |||
| Salt Lake County | 1,031,697 | 1,046,461 | 1,060,336 | 1,070,815 | 1,080,905 | 1,094,681 | 1,108,910 | 1,128,271 | 1,142,081 | 1,152,960 | |||
| San Juan County | 14,771 | 15,037 | 15,448 | 15,578 | 15,782 | 15,919 | 16,324 | 16,333 | 16,490 | 16,680 | |||
| Sanpete County | 27,907 | 28,351 | 28,485 | 28,632 | 28,705 | 29,089 | 29,490 | 30,032 | 30,578 | 31,003 | |||
| Sevier County | 20,814 | 20,893 | 21,053 | 21,021 | 21,102 | 21,240 | 21,519 | 21,765 | 21,928 | 22,219 | |||
| Summit County | 36,562 | 37,396 | 37,936 | 38,212 | 38,678 | 39,280 | 40,051 | 40,771 | 41,285 | 41,824 | |||
| Tooele County | 58,358 | 59,151 | 60,131 | 61,367 | 62,184 | 63,266 | 65,290 | 67,133 | 68,858 | 70,889 | |||
| Uintah County | 32,760 | 33,943 | 35,047 | 36,146 | 36,981 | 37,398 | 36,583 | 36,612 | 36,921 | 36,973 | |||
| Utah County | 518,872 | 532,753 | 544,892 | 554,405 | 567,218 | 585,719 | 603,385 | 617,735 | 633,582 | 651,409 | |||
| Wasatch County | 23,652 | 24,484 | 25,542 | 26,390 | 27,344 | 28,616 | 29,998 | 31,224 | 32,138 | 32,866 | |||
| Washington County | 138,579 | 141,797 | 144,061 | 147,061 | 150,508 | 154,615 | 160,371 | 165,592 | 171,042 | 180,550 | |||
| Wayne County | 2,782 | 2,766 | 2,773 | 2,748 | 2,740 | 2,725 | 2,719 | 2,738 | 2,752 | 2,754 | |||
| Weber County | 231,833 | 233,819 | 236,391 | 237,921 | 239,588 | 242,753 | 245,687 | 248,835 | 251,571 | 253,455 |
Education
| Education Attained | Median Annual Earnings | Unemployment Rate (%) | Place the "Education and Unemployment" Chart Here for Grading | ||
| Less than a high school diploma | $28,756 | 5.6 | |||
| High-school graduate | $37,960 | 4.1 | |||
| Some college, no degree | $41,704 | 3.7 | |||
| Associate degree | $44,824 | 2.8 | |||
| Bachelor's degree | $62,296 | 2.2 | |||
| Master's degree | $74,568 | 2.1 | |||
| Professional degree | $97,968 | 1.5 | |||
| Doctoral degree | $94,900 | 1.6 | |||
| Place the "Education and Income" Chart Here for Grading | |||||
Project 5 instructions.docx
1. Tables Assessment
April owns a rental house. She rents the house to college students and includes electricity in the cost of rent. She would like you to help her do some analysis on the costs over the last year to decide if she wants to increase the rental cost based on this expense. The data on the "Electric" worksheet includes the number of residents in the house and the Kilowatt hours (KWh) used for the previous year. Complete the tasks to help April make her decision.
1.1
Create a table from the existing data in range B2:G14 of the "Electric" worksheet. Notice that row 2 (the first row of the data) has column headings.
1.2
Add a calculated column to the end of the table.
a. Enter the column heading "KWh Cost".
b. The column should calculate the cost of the total KWh used based on the cost per KWh for each month.
c. The total cost is calculated by muliplying the KWh by Cost per KWh for each row of the table.
1.3
Add a calculated column to the end of the table (column I).
a. Enter the column heading "KWh per Resident".
b. The KWh per resident is calculated by dividing the KWh by the Residents for each row of the table.
1.4
Sort the table by "KWh" in descending order.
1.5
Add a totals row to the table to calculate the average of the "KWh per Resident"
1.6
Add a second total to the table to calculate the sum of KWh used during the year.
1.7
Filter the items on the table to display only the months with an average temperature greater than 80 degrees.
Project 5.xlsx
Electric
| Month | Billing Days | Residents | Average Temp | KWh | Cost per KWh | |
| Jan | 31 | 3 | 40 | 990 | $ 0.1356 | |
| Feb | 31 | 3 | 45 | 902 | $ 0.1356 | |
| Mar | 29 | 4 | 56 | 850 | $ 0.1285 | |
| Apr | 31 | 4 | 67 | 893 | $ 0.1250 | |
| May | 31 | 2 | 76 | 809 | $ 0.1156 | |
| Jun | 31 | 2 | 85 | 822 | $ 0.1156 | |
| Jul | 29 | 2 | 89 | 851 | $ 0.1185 | |
| Aug | 33 | 3 | 89 | 950 | $ 0.1199 | |
| Sep | 30 | 3 | 80 | 830 | $ 0.1225 | |
| Oct | 30 | 4 | 68 | 1016 | $ 0.1251 | |
| Nov | 29 | 3 | 55 | 945 | $ 0.1285 | |
| Dec | 30 | 3 | 42 | 915 | $ 0.1315 |
Project 6 instructions.docx
1. Conditional Formatting Assessment
Twitch is a livestreaming video service. While you can find a variety of different types of content on Twitch, they are primarily known for video game streaming. Top streamers can earn millions of dollars a year.
The Value, Top, and Symbols worksheets all contain data on the top 200 Twitch gaming streamers in 2020. On the Value worksheet, use conditional formatting based on target values to examine the data. Use conditional formatting based on exceptional values to analyze the data on the Top worksheet. Finally, use the Data Bars, Color Scales, and Icon Sets to examine the data on the Symbols worksheet.
1.1
Use conditional formatting to highlight the number of followers for streamers with more than 500000 followers.
a. Apply the conditional formatting to range G3:G202.
b. Highlight values over 500000 with light green background and dark green text.
1.2
Use conditional formatting to highlight the number of streaming minutes for streamers with less than 100000 minutes streamed.
a. Apply the conditional formatting to range D3:D202.
b. Highlight values less than 100000 minutes streamed with light red background and dark red text.
1.3
Use conditional formatting to highlight the value for average viewers of streamers with between 5000 and 8000 average viewers.
a. Apply the conditional formatting to range F3:F202.
b. Highlight values between 5000 and 8000 average viewers with light yellow background and dark yellow text.
1.4
Use conditional formatting to highlight the value for language of streamers who speak Czech.
a. Apply the conditional formatting to range H3:H202.
b. Highlight values that contain the text "Czech" (without the quotes) with light red background and dark red text.
1.5
Use conditional formatting to highlight the top 30 values for minutes of watch time.
a. Apply the conditional formatting to range C3:C202.
b. Highlight the top 30 values for watch time with light green background and dark green text.
1.6
Use conditional formatting to highlight the bottom 15 % of values for minutes of stream time.
a. Apply the conditional formatting to range D3:D202.
b. Highlight the bottom 15% of values for stream time with light red background and dark red text.
1.7
Use conditional formatting to highlight the above average values for peak viewers.
a. Apply the conditional formatting to range E3:E202.
b. Highlight the above average values for peak viewers with light yellow background and dark yellow text.
1.8
Use Data Bars to highlight the number of peak users for each streamer.
a. Apply the conditional formatting to range E3:E202.
b. Select the blue data bars option.
1.9
Use Color Scales to highlight the number of followers for each streamer.
a. Apply the conditional formatting to range G3:G202.
b. Select a three-color option.
1.10
Use Icon Sets to highlight the number of minutes streaming for each streamer.
a. Apply the conditional formatting to range D3:D202.
b. Use the 3 Arrows (Colored) option.
Project 6.xlsx
Value
| Channel | Watch Time | Stream Time | Peak Viewers | Average Viewers | Followers | Language | |
| aceu | 744620970 | 118125 | 26141 | 6328 | 859439 | English | |
| AdmiralBahroo | 1188645990 | 141210 | 21053 | 8152 | 778055 | English | |
| AdmiralBulldog | 972317520 | 154845 | 16681 | 6198 | 694253 | English | |
| Agraelus | 779867430 | 169515 | 23555 | 4642 | 414951 | Czech | |
| alanzoka | 2055003870 | 103770 | 89153 | 19560 | 3445134 | Portuguese | |
| allkeyshop_tv | 663185955 | 487005 | 6075 | 1361 | 67472 | English | |
| Amouranth | 618067800 | 235170 | 13495 | 2560 | 1707804 | English | |
| Anomaly | 2865429915 | 92880 | 125408 | 12377 | 2607076 | English | |
| Anton | 581034300 | 142890 | 137531 | 3199 | 207484 | English | |
| Asmongold | 3668799075 | 82260 | 263720 | 42414 | 1563438 | English | |
| auronplay | 2410022550 | 40575 | 170115 | 53986 | 3983847 | Spanish | |
| Baiano | 859718520 | 85860 | 107069 | 9229 | 425797 | Portuguese | |
| benjyfishy | 580787955 | 34350 | 75491 | 14423 | 1739112 | English | |
| BeyondTheSummit | 1339097490 | 505080 | 116547 | 2635 | 923689 | English | |
| BLASTPremier | 753808200 | 25260 | 113167 | 24689 | 501371 | English | |
| blusewilly_retry | 618755280 | 100515 | 17585 | 5941 | 374480 | Chinese | |
| BobRoss | 558170835 | 238035 | 11659 | 2175 | 1519266 | English | |
| bratishkinoff | 646333065 | 60795 | 56790 | 10626 | 1128907 | Russian | |
| Brunenger | 559915035 | 183225 | 64308 | 2844 | 659652 | Spanish | |
| Bugha | 1324519320 | 100470 | 66311 | 12982 | 2942212 | English | |
| buster | 884353800 | 59295 | 97838 | 14195 | 1087377 | Russian | |
| C_a_k_e | 588662010 | 129660 | 17317 | 4403 | 302633 | Russian | |
| CasinoDaddy | 577240710 | 267465 | 6524 | 2168 | 151098 | English | |
| Castro_1021 | 1845157080 | 100215 | 125133 | 17779 | 2411995 | English | |
| CDNThe3rd | 722562675 | 134280 | 55752 | 4919 | 2009972 | English | |
| Cellbit | 817373955 | 103095 | 68813 | 8264 | 1293451 | Portuguese | |
| Chap | 550951215 | 157545 | 22571 | 3430 | 1272899 | English | |
| chocoTaco | 620395515 | 160830 | 30514 | 3846 | 1134153 | English | |
| ClassyBeef | 558883590 | 273660 | 33624 | 1941 | 108623 | English | |
| Clix | 1256647110 | 89760 | 81926 | 12996 | 2035180 | English | |
| cloakzy | 748023225 | 101670 | 40497 | 6743 | 2138294 | English | |
| CohhCarnage | 2029212570 | 175230 | 43615 | 11343 | 1264808 | English | |
| coscu | 622424175 | 77160 | 80444 | 8919 | 1865296 | Spanish | |
| CriticalRole | 539495145 | 21300 | 110800 | 17689 | 571210 | English | |
| csgomc_ru | 1308967860 | 77955 | 364816 | 17020 | 492954 | Russian | |
| CSRuHub | 540556545 | 110880 | 106003 | 5044 | 511431 | Russian | |
| dakotaz | 978947160 | 132615 | 43397 | 7112 | 4520305 | English | |
| DansGaming | 653181210 | 187530 | 33646 | 3270 | 817365 | English | |
| dasMEHDI | 1172969025 | 231465 | 47683 | 5013 | 299048 | English | |
| ddahyoni | 711864630 | 152445 | 17253 | 4534 | 322895 | Korean | |
| Destiny | 650910525 | 162690 | 24101 | 3894 | 571183 | English | |
| Diegosaurs | 558587535 | 128580 | 32463 | 4150 | 521201 | English | |
| dogdog | 622199835 | 103335 | 24727 | 5856 | 594239 | English | |
| Domingo | 662502810 | 65610 | 102022 | 11423 | 829700 | French | |
| dota2mc_ru | 1464683175 | 66675 | 182869 | 19495 | 428284 | Russian | |
| Dota2RuHub | 1330625430 | 92160 | 105359 | 13189 | 777510 | Russian | |
| dota2ti | 1017577605 | 6315 | 483530 | 147643 | 663297 | English | |
| dota2ti_ru | 812538090 | 6195 | 457060 | 126232 | 541644 | Russian | |
| DrDisrespect | 1839882465 | 73065 | 97540 | 23794 | 4450718 | English | |
| DreadzTV | 715644660 | 109725 | 35865 | 6249 | 675908 | Russian | |
| DreamHackCS | 1052904720 | 314595 | 212201 | 5001 | 1801697 | English | |
| DrLupo | 1517612010 | 172350 | 90696 | 8311 | 4115083 | English | |
| Elajjaz | 726000045 | 145755 | 13080 | 4922 | 346566 | English | |
| elded | 853049385 | 110940 | 44758 | 7699 | 2601858 | Spanish | |
| ElmiilloR | 686456910 | 126105 | 45726 | 5163 | 426716 | Spanish | |
| Elraenn | 726379485 | 51150 | 65543 | 13224 | 1223076 | Turkish | |
| EsfandTV | 888938940 | 189045 | 29597 | 4393 | 471970 | English | |
| ESL_CSGO | 3970318140 | 517740 | 300575 | 7714 | 3944850 | English | |
| ESL_DOTA2 | 661049190 | 212010 | 99858 | 4714 | 337177 | English | |
| Evelone192 | 1474742220 | 83010 | 106900 | 16422 | 1075101 | Russian | |
| Fextralife | 3301867485 | 147885 | 68795 | 18985 | 508816 | English | |
| forsen | 1106781045 | 109140 | 33966 | 10080 | 1308165 | English | |
| fps_shaka | 1131509385 | 215160 | 26572 | 5195 | 303671 | Japanese | |
| Fresh | 1464179820 | 147660 | 57431 | 9728 | 3135667 | English | |
| GamesDoneQuick | 1619144100 | 87450 | 234826 | 6734 | 1724316 | English | |
| Gaules | 5644590915 | 515280 | 387315 | 10976 | 1767635 | Portuguese | |
| Giantwaffle | 612594165 | 165525 | 36340 | 3429 | 878934 | English | |
| Gladd | 543954570 | 171195 | 54477 | 2779 | 337972 | English | |
| GMHikaru | 554249955 | 68355 | 46106 | 7155 | 505361 | English | |
| godjj | 544706325 | 126705 | 12461 | 4378 | 331744 | Chinese | |
| Gorgc | 1252711830 | 141135 | 56449 | 8683 | 391726 | English | |
| Gotaga | 1538511315 | 141675 | 81644 | 10750 | 2401580 | French | |
| Greekgodx | 561616335 | 89745 | 45741 | 7083 | 1278824 | English | |
| GRONKH | 1017544335 | 54645 | 100330 | 17860 | 1216020 | German | |
| H2P_Gucio | 575998575 | 157995 | 10011 | 3581 | 220488 | Polish | |
| handongsuk | 1621667925 | 127815 | 44976 | 12869 | 385250 | Korean | |
| hanryang1125 | 2186662470 | 181230 | 26999 | 12201 | 494445 | Korean | |
| HasanAbi | 1339344945 | 193560 | 44649 | 6543 | 470123 | English | |
| ibai | 1412913285 | 57795 | 173238 | 22837 | 1894953 | Spanish | |
| imaqtpie | 596368095 | 167190 | 26087 | 3478 | 2652018 | English | |
| IzakOOO | 717096330 | 129165 | 43050 | 4463 | 1461767 | Polish | |
| Jahrein | 566176425 | 61890 | 43683 | 8929 | 1422862 | Turkish | |
| Jinnytty | 569601090 | 151815 | 15190 | 3607 | 372334 | English | |
| jinu6734 | 597275955 | 121545 | 12810 | 4710 | 274875 | Korean | |
| JLTomy | 1228169940 | 121455 | 42079 | 10053 | 428073 | French | |
| jovirone | 553283745 | 96450 | 29272 | 5632 | 1089830 | Portuguese | |
| juansguarnizo | 849083325 | 123780 | 45631 | 6039 | 1204773 | Spanish | |
| jukes | 628079220 | 76605 | 24263 | 8165 | 1327059 | Portuguese | |
| Kamet0 | 600882645 | 130620 | 70983 | 4560 | 565661 | French | |
| KendineMuzisyen | 567374295 | 58545 | 43422 | 9583 | 1372290 | Turkish | |
| kimdoe | 549244755 | 134985 | 11759 | 4062 | 287639 | Korean | |
| Kitboga | 656365305 | 80760 | 20913 | 7394 | 772055 | English | |
| Klean | 545108145 | 169605 | 101124 | 2801 | 276242 | English | |
| LCK | 1351758525 | 37140 | 171861 | 36030 | 934688 | English | |
| LCK_Korea | 1916365860 | 47325 | 140557 | 39848 | 619382 | Korean | |
| LCS | 1461310140 | 31125 | 214124 | 46459 | 1162746 | English | |
| LEC | 1470431925 | 45660 | 305119 | 28830 | 973727 | English | |
| lestream | 955346835 | 253395 | 47638 | 3652 | 883706 | French | |
| LIRIK | 2832930285 | 128490 | 89170 | 21739 | 2666382 | English | |
| Locklear | 619247415 | 108450 | 50103 | 5512 | 824676 | French | |
| lol_ambition | 639445965 | 113415 | 57254 | 5332 | 362297 | Korean | |
| lol_woolf | 532969650 | 50910 | 73800 | 9633 | 308528 | Korean | |
| loltyler1 | 2928356940 | 122490 | 89387 | 22381 | 3530767 | English | |
| Lord_Kebun | 1943299035 | 153720 | 34830 | 12367 | 434200 | English | |
| LPL | 850636305 | 48765 | 146577 | 17573 | 502467 | English | |
| LVNDMARK | 788421150 | 199350 | 96236 | 3688 | 248829 | English | |
| LVPes | 1115650275 | 90960 | 233009 | 12947 | 587677 | Spanish | |
| Mathil1 | 561997440 | 134715 | 25866 | 3953 | 293595 | English | |
| Maximilian_DOOD | 1023316710 | 110040 | 43253 | 9235 | 833047 | English | |
| Method | 905107560 | 230940 | 148350 | 4135 | 401400 | English | |
| Mithrain | 530456265 | 105540 | 40183 | 4990 | 1258713 | Turkish | |
| Mizkif | 1052047935 | 123120 | 32671 | 7899 | 591653 | English | |
| mobilmobil | 652685055 | 126120 | 16497 | 5303 | 446426 | Chinese | |
| MontanaBlack88 | 2408460990 | 67740 | 181600 | 33514 | 2911316 | German | |
| MOONMOON | 1527882945 | 124680 | 24892 | 11220 | 923448 | English | |
| muse_tw | 625892895 | 446655 | 16702 | 1397 | 121053 | Chinese | |
| MYM_ALKAPONE | 599850495 | 97830 | 26221 | 5948 | 770535 | Spanish | |
| Myth | 1479214575 | 134760 | 122552 | 9396 | 6726893 | English | |
| Nick28T | 556741020 | 183660 | 15155 | 3024 | 977377 | English | |
| NickEh30 | 1148114400 | 117885 | 85073 | 9702 | 1660204 | English | |
| NICKMERCS | 3360675195 | 136275 | 115633 | 24181 | 4074287 | English | |
| Nightblue3 | 899215845 | 118980 | 17738 | 7234 | 2641880 | English | |
| nl_Kripp | 1470897720 | 155895 | 29316 | 9256 | 1379123 | English | |
| NOBRU | 888211260 | 38655 | 132224 | 22070 | 1549722 | Portuguese | |
| nokduro | 555637890 | 140670 | 15858 | 4040 | 165823 | Korean | |
| NoWay4u_Sir | 1234567245 | 139920 | 24286 | 8479 | 383892 | German | |
| OgamingLoL | 1483207890 | 496950 | 204491 | 3020 | 523758 | French | |
| ONSCREEN | 1197130335 | 134880 | 88516 | 4134 | 918654 | English | |
| OverwatchLeague | 805163370 | 24480 | 254493 | 33132 | 1796619 | English | |
| Papaplatte | 1105525440 | 125550 | 42230 | 8546 | 919026 | German | |
| Pestily | 1659741015 | 138300 | 168112 | 8481 | 616168 | English | |
| PlayHearthstone | 661075170 | 41175 | 43877 | 13154 | 825727 | English | |
| pokimane | 964334055 | 56505 | 112160 | 16026 | 5367605 | English | |
| POW3Rtv | 721548885 | 177885 | 69009 | 3836 | 1080764 | Italian | |
| Quin69 | 1186941750 | 174270 | 36742 | 6616 | 538532 | English | |
| Rainbow6 | 1031011170 | 82380 | 135471 | 11535 | 1501197 | English | |
| Rakin | 842581305 | 144510 | 51854 | 5333 | 1258173 | Portuguese | |
| RatedEpicz | 582401145 | 175920 | 11860 | 3336 | 134757 | English | |
| RATIRL | 649761570 | 145050 | 14480 | 4420 | 423002 | English | |
| RebirthzTV | 548041425 | 118920 | 14578 | 4629 | 276694 | Thai | |
| Reborn_Live | 578122875 | 32100 | 57849 | 17488 | 697007 | Spanish | |
| riotgames | 2674646715 | 80820 | 639375 | 20960 | 4487489 | English | |
| RiotGamesBrazil | 1228613130 | 38370 | 255542 | 25918 | 1011924 | Portuguese | |
| RocketLeague | 1322448480 | 33540 | 206681 | 36086 | 1409120 | English | |
| ROSHTEIN | 1435735725 | 118995 | 45843 | 11717 | 381918 | English | |
| Rubius | 2588632635 | 58275 | 240096 | 42948 | 5751354 | Spanish | |
| Sacriel | 1002681105 | 163095 | 66781 | 5573 | 672403 | English | |
| saddummy | 1241997345 | 182310 | 25482 | 6681 | 580794 | Korean | |
| Sardoche | 1361024835 | 164235 | 144066 | 8066 | 746865 | French | |
| Scarra | 864157695 | 138360 | 27421 | 6060 | 1242014 | English | |
| Sfory | 612617325 | 73590 | 75219 | 3197 | 457502 | Russian | |
| Shlorox | 625142130 | 115650 | 13945 | 5216 | 331632 | German | |
| shroud | 888505170 | 30240 | 471281 | 29612 | 7744066 | English | |
| shuteye_orange | 728551080 | 325935 | 7441 | 2217 | 85247 | Chinese | |
| SilverName | 1006608690 | 95625 | 29927 | 10618 | 614395 | Russian | |
| SkipNhO | 553663800 | 131580 | 23408 | 3875 | 1076499 | Portuguese | |
| SkipNhOLIVE | 600910875 | 498765 | 7940 | 1196 | 324765 | Portuguese | |
| SmiteGame | 586925850 | 344055 | 33245 | 1588 | 535211 | English | |
| sneakylol | 1149209820 | 174885 | 22759 | 6775 | 1659108 | English | |
| sodapoppin | 2329440420 | 115305 | 107833 | 19659 | 2786162 | English | |
| Solary | 1546597380 | 486510 | 24470 | 3187 | 493207 | French | |
| SolaryFortnite | 1223349555 | 381735 | 46710 | 3180 | 1478270 | French | |
| SolaryHS | 827452485 | 460065 | 17513 | 1802 | 149073 | French | |
| Squeezie | 667977780 | 29775 | 158972 | 19260 | 2149306 | French | |
| StarLadder_cs_en | 1088832810 | 32880 | 329195 | 29956 | 820675 | English | |
| StarLadder5 | 580541850 | 41715 | 189859 | 13089 | 1029203 | Russian | |
| Stray228 | 972961650 | 93750 | 36971 | 10290 | 773712 | Russian | |
| stylishnoob4 | 1029543660 | 118515 | 25263 | 8485 | 354579 | Japanese | |
| summit1g | 6091677300 | 211845 | 310998 | 25610 | 5310163 | English | |
| Swagg | 790021440 | 108375 | 74199 | 6309 | 784966 | English | |
| Symfuhny | 1076179485 | 137400 | 45671 | 7327 | 2355063 | English | |
| SypherPK | 1016450160 | 145230 | 130401 | 6553 | 3611359 | English | |
| TeePee | 789698115 | 170010 | 78741 | 4410 | 520519 | English | |
| TFBlade | 1394312895 | 141285 | 35833 | 9117 | 1008040 | English | |
| Tfue | 3671000070 | 123660 | 285644 | 29602 | 8938903 | English | |
| TheGrefg | 1757406750 | 54855 | 538444 | 28887 | 3795667 | Spanish | |
| TheRealKnossi | 1811696100 | 56010 | 288459 | 24595 | 1260160 | German | |
| Thijs | 794621265 | 108720 | 24923 | 7180 | 755116 | English | |
| TimTheTatman | 2834436990 | 108780 | 142067 | 25664 | 5265659 | English | |
| tmxk319 | 857951685 | 116400 | 71933 | 7173 | 427926 | Korean | |
| Trainwreckstv | 1021699920 | 148425 | 49379 | 7134 | 728097 | English | |
| Trymacs | 1184154975 | 107880 | 50957 | 10735 | 1607134 | German | |
| UberHaxorNova | 615472275 | 181950 | 7808 | 3247 | 421256 | English | |
| uzra | 812362125 | 208785 | 14181 | 3683 | 185506 | Chinese | |
| Vader | 1110952500 | 184305 | 16289 | 5913 | 424374 | English | |
| Vinesauce | 536989080 | 86790 | 19065 | 6125 | 442493 | English | |
| WePlayEsport_EN | 704823000 | 107745 | 87751 | 6127 | 175061 | English | |
| WePlayEsport_RU | 853324635 | 92970 | 115737 | 8627 | 346934 | Russian | |
| woowakgood | 650364705 | 158850 | 14177 | 4100 | 591500 | Korean | |
| wtcN | 582125625 | 77385 | 73861 | 7438 | 1852272 | Turkish | |
| x2Twins | 595707975 | 125745 | 31874 | 4167 | 1288969 | English | |
| Xayoo_ | 575138175 | 91260 | 23935 | 6091 | 572789 | Polish | |
| xQcOW | 6196161750 | 215250 | 222720 | 27716 | 3246298 | English | |
| Yassuo | 1347412425 | 103905 | 70587 | 10531 | 1878416 | English | |
| ybicanoooobov | 938816460 | 114765 | 17036 | 8255 | 523512 | Russian | |
| YoDa | 1690237110 | 135675 | 123796 | 12868 | 1792625 | Portuguese | |
| Yogscast | 644580630 | 443130 | 54885 | 1384 | 961860 | English | |
| ZanoXVII | 610609920 | 95730 | 30648 | 6073 | 514866 | Italian | |
| ZeratoR | 1011013035 | 84960 | 262273 | 10516 | 880728 | French | |
| zilioner | 601906185 | 97545 | 43164 | 5843 | 465887 | Korean |
Top
| Channel | Watch Time | Stream Time | Peak Viewers | Average Viewers | Followers | Language | |
| aceu | 744620970 | 118125 | 26141 | 6328 | 859439 | English | |
| AdmiralBahroo | 1188645990 | 141210 | 21053 | 8152 | 778055 | English | |
| AdmiralBulldog | 972317520 | 154845 | 16681 | 6198 | 694253 | English | |
| Agraelus | 779867430 | 169515 | 23555 | 4642 | 414951 | Czech | |
| alanzoka | 2055003870 | 103770 | 89153 | 19560 | 3445134 | Portuguese | |
| allkeyshop_tv | 663185955 | 487005 | 6075 | 1361 | 67472 | English | |
| Amouranth | 618067800 | 235170 | 13495 | 2560 | 1707804 | English | |
| Anomaly | 2865429915 | 92880 | 125408 | 12377 | 2607076 | English | |
| Anton | 581034300 | 142890 | 137531 | 3199 | 207484 | English | |
| Asmongold | 3668799075 | 82260 | 263720 | 42414 | 1563438 | English | |
| auronplay | 2410022550 | 40575 | 170115 | 53986 | 3983847 | Spanish | |
| Baiano | 859718520 | 85860 | 107069 | 9229 | 425797 | Portuguese | |
| benjyfishy | 580787955 | 34350 | 75491 | 14423 | 1739112 | English | |
| BeyondTheSummit | 1339097490 | 505080 | 116547 | 2635 | 923689 | English | |
| BLASTPremier | 753808200 | 25260 | 113167 | 24689 | 501371 | English | |
| blusewilly_retry | 618755280 | 100515 | 17585 | 5941 | 374480 | Chinese | |
| BobRoss | 558170835 | 238035 | 11659 | 2175 | 1519266 | English | |
| bratishkinoff | 646333065 | 60795 | 56790 | 10626 | 1128907 | Russian | |
| Brunenger | 559915035 | 183225 | 64308 | 2844 | 659652 | Spanish | |
| Bugha | 1324519320 | 100470 | 66311 | 12982 | 2942212 | English | |
| buster | 884353800 | 59295 | 97838 | 14195 | 1087377 | Russian | |
| C_a_k_e | 588662010 | 129660 | 17317 | 4403 | 302633 | Russian | |
| CasinoDaddy | 577240710 | 267465 | 6524 | 2168 | 151098 | English | |
| Castro_1021 | 1845157080 | 100215 | 125133 | 17779 | 2411995 | English | |
| CDNThe3rd | 722562675 | 134280 | 55752 | 4919 | 2009972 | English | |
| Cellbit | 817373955 | 103095 | 68813 | 8264 | 1293451 | Portuguese | |
| Chap | 550951215 | 157545 | 22571 | 3430 | 1272899 | English | |
| chocoTaco | 620395515 | 160830 | 30514 | 3846 | 1134153 | English | |
| ClassyBeef | 558883590 | 273660 | 33624 | 1941 | 108623 | English | |
| Clix | 1256647110 | 89760 | 81926 | 12996 | 2035180 | English | |
| cloakzy | 748023225 | 101670 | 40497 | 6743 | 2138294 | English | |
| CohhCarnage | 2029212570 | 175230 | 43615 | 11343 | 1264808 | English | |
| coscu | 622424175 | 77160 | 80444 | 8919 | 1865296 | Spanish | |
| CriticalRole | 539495145 | 21300 | 110800 | 17689 | 571210 | English | |
| csgomc_ru | 1308967860 | 77955 | 364816 | 17020 | 492954 | Russian | |
| CSRuHub | 540556545 | 110880 | 106003 | 5044 | 511431 | Russian | |
| dakotaz | 978947160 | 132615 | 43397 | 7112 | 4520305 | English | |
| DansGaming | 653181210 | 187530 | 33646 | 3270 | 817365 | English | |
| dasMEHDI | 1172969025 | 231465 | 47683 | 5013 | 299048 | English | |
| ddahyoni | 711864630 | 152445 | 17253 | 4534 | 322895 | Korean | |
| Destiny | 650910525 | 162690 | 24101 | 3894 | 571183 | English | |
| Diegosaurs | 558587535 | 128580 | 32463 | 4150 | 521201 | English | |
| dogdog | 622199835 | 103335 | 24727 | 5856 | 594239 | English | |
| Domingo | 662502810 | 65610 | 102022 | 11423 | 829700 | French | |
| dota2mc_ru | 1464683175 | 66675 | 182869 | 19495 | 428284 | Russian | |
| Dota2RuHub | 1330625430 | 92160 | 105359 | 13189 | 777510 | Russian | |
| dota2ti | 1017577605 | 6315 | 483530 | 147643 | 663297 | English | |
| dota2ti_ru | 812538090 | 6195 | 457060 | 126232 | 541644 | Russian | |
| DrDisrespect | 1839882465 | 73065 | 97540 | 23794 | 4450718 | English | |
| DreadzTV | 715644660 | 109725 | 35865 | 6249 | 675908 | Russian | |
| DreamHackCS | 1052904720 | 314595 | 212201 | 5001 | 1801697 | English | |
| DrLupo | 1517612010 | 172350 | 90696 | 8311 | 4115083 | English | |
| Elajjaz | 726000045 | 145755 | 13080 | 4922 | 346566 | English | |
| elded | 853049385 | 110940 | 44758 | 7699 | 2601858 | Spanish | |
| ElmiilloR | 686456910 | 126105 | 45726 | 5163 | 426716 | Spanish | |
| Elraenn | 726379485 | 51150 | 65543 | 13224 | 1223076 | Turkish | |
| EsfandTV | 888938940 | 189045 | 29597 | 4393 | 471970 | English | |
| ESL_CSGO | 3970318140 | 517740 | 300575 | 7714 | 3944850 | English | |
| ESL_DOTA2 | 661049190 | 212010 | 99858 | 4714 | 337177 | English | |
| Evelone192 | 1474742220 | 83010 | 106900 | 16422 | 1075101 | Russian | |
| Fextralife | 3301867485 | 147885 | 68795 | 18985 | 508816 | English | |
| forsen | 1106781045 | 109140 | 33966 | 10080 | 1308165 | English | |
| fps_shaka | 1131509385 | 215160 | 26572 | 5195 | 303671 | Japanese | |
| Fresh | 1464179820 | 147660 | 57431 | 9728 | 3135667 | English | |
| GamesDoneQuick | 1619144100 | 87450 | 234826 | 6734 | 1724316 | English | |
| Gaules | 5644590915 | 515280 | 387315 | 10976 | 1767635 | Portuguese | |
| Giantwaffle | 612594165 | 165525 | 36340 | 3429 | 878934 | English | |
| Gladd | 543954570 | 171195 | 54477 | 2779 | 337972 | English | |
| GMHikaru | 554249955 | 68355 | 46106 | 7155 | 505361 | English | |
| godjj | 544706325 | 126705 | 12461 | 4378 | 331744 | Chinese | |
| Gorgc | 1252711830 | 141135 | 56449 | 8683 | 391726 | English | |
| Gotaga | 1538511315 | 141675 | 81644 | 10750 | 2401580 | French | |
| Greekgodx | 561616335 | 89745 | 45741 | 7083 | 1278824 | English | |
| GRONKH | 1017544335 | 54645 | 100330 | 17860 | 1216020 | German | |
| H2P_Gucio | 575998575 | 157995 | 10011 | 3581 | 220488 | Polish | |
| handongsuk | 1621667925 | 127815 | 44976 | 12869 | 385250 | Korean | |
| hanryang1125 | 2186662470 | 181230 | 26999 | 12201 | 494445 | Korean | |
| HasanAbi | 1339344945 | 193560 | 44649 | 6543 | 470123 | English | |
| ibai | 1412913285 | 57795 | 173238 | 22837 | 1894953 | Spanish | |
| imaqtpie | 596368095 | 167190 | 26087 | 3478 | 2652018 | English | |
| IzakOOO | 717096330 | 129165 | 43050 | 4463 | 1461767 | Polish | |
| Jahrein | 566176425 | 61890 | 43683 | 8929 | 1422862 | Turkish | |
| Jinnytty | 569601090 | 151815 | 15190 | 3607 | 372334 | English | |
| jinu6734 | 597275955 | 121545 | 12810 | 4710 | 274875 | Korean | |
| JLTomy | 1228169940 | 121455 | 42079 | 10053 | 428073 | French | |
| jovirone | 553283745 | 96450 | 29272 | 5632 | 1089830 | Portuguese | |
| juansguarnizo | 849083325 | 123780 | 45631 | 6039 | 1204773 | Spanish | |
| jukes | 628079220 | 76605 | 24263 | 8165 | 1327059 | Portuguese | |
| Kamet0 | 600882645 | 130620 | 70983 | 4560 | 565661 | French | |
| KendineMuzisyen | 567374295 | 58545 | 43422 | 9583 | 1372290 | Turkish | |
| kimdoe | 549244755 | 134985 | 11759 | 4062 | 287639 | Korean | |
| Kitboga | 656365305 | 80760 | 20913 | 7394 | 772055 | English | |
| Klean | 545108145 | 169605 | 101124 | 2801 | 276242 | English | |
| LCK | 1351758525 | 37140 | 171861 | 36030 | 934688 | English | |
| LCK_Korea | 1916365860 | 47325 | 140557 | 39848 | 619382 | Korean | |
| LCS | 1461310140 | 31125 | 214124 | 46459 | 1162746 | English | |
| LEC | 1470431925 | 45660 | 305119 | 28830 | 973727 | English | |
| lestream | 955346835 | 253395 | 47638 | 3652 | 883706 | French | |
| LIRIK | 2832930285 | 128490 | 89170 | 21739 | 2666382 | English | |
| Locklear | 619247415 | 108450 | 50103 | 5512 | 824676 | French | |
| lol_ambition | 639445965 | 113415 | 57254 | 5332 | 362297 | Korean | |
| lol_woolf | 532969650 | 50910 | 73800 | 9633 | 308528 | Korean | |
| loltyler1 | 2928356940 | 122490 | 89387 | 22381 | 3530767 | English | |
| Lord_Kebun | 1943299035 | 153720 | 34830 | 12367 | 434200 | English | |
| LPL | 850636305 | 48765 | 146577 | 17573 | 502467 | English | |
| LVNDMARK | 788421150 | 199350 | 96236 | 3688 | 248829 | English | |
| LVPes | 1115650275 | 90960 | 233009 | 12947 | 587677 | Spanish | |
| Mathil1 | 561997440 | 134715 | 25866 | 3953 | 293595 | English | |
| Maximilian_DOOD | 1023316710 | 110040 | 43253 | 9235 | 833047 | English | |
| Method | 905107560 | 230940 | 148350 | 4135 | 401400 | English | |
| Mithrain | 530456265 | 105540 | 40183 | 4990 | 1258713 | Turkish | |
| Mizkif | 1052047935 | 123120 | 32671 | 7899 | 591653 | English | |
| mobilmobil | 652685055 | 126120 | 16497 | 5303 | 446426 | Chinese | |
| MontanaBlack88 | 2408460990 | 67740 | 181600 | 33514 | 2911316 | German | |
| MOONMOON | 1527882945 | 124680 | 24892 | 11220 | 923448 | English | |
| muse_tw | 625892895 | 446655 | 16702 | 1397 | 121053 | Chinese | |
| MYM_ALKAPONE | 599850495 | 97830 | 26221 | 5948 | 770535 | Spanish | |
| Myth | 1479214575 | 134760 | 122552 | 9396 | 6726893 | English | |
| Nick28T | 556741020 | 183660 | 15155 | 3024 | 977377 | English | |
| NickEh30 | 1148114400 | 117885 | 85073 | 9702 | 1660204 | English | |
| NICKMERCS | 3360675195 | 136275 | 115633 | 24181 | 4074287 | English | |
| Nightblue3 | 899215845 | 118980 | 17738 | 7234 | 2641880 | English | |
| nl_Kripp | 1470897720 | 155895 | 29316 | 9256 | 1379123 | English | |
| NOBRU | 888211260 | 38655 | 132224 | 22070 | 1549722 | Portuguese | |
| nokduro | 555637890 | 140670 | 15858 | 4040 | 165823 | Korean | |
| NoWay4u_Sir | 1234567245 | 139920 | 24286 | 8479 | 383892 | German | |
| OgamingLoL | 1483207890 | 496950 | 204491 | 3020 | 523758 | French | |
| ONSCREEN | 1197130335 | 134880 | 88516 | 4134 | 918654 | English | |
| OverwatchLeague | 805163370 | 24480 | 254493 | 33132 | 1796619 | English | |
| Papaplatte | 1105525440 | 125550 | 42230 | 8546 | 919026 | German | |
| Pestily | 1659741015 | 138300 | 168112 | 8481 | 616168 | English | |
| PlayHearthstone | 661075170 | 41175 | 43877 | 13154 | 825727 | English | |
| pokimane | 964334055 | 56505 | 112160 | 16026 | 5367605 | English | |
| POW3Rtv | 721548885 | 177885 | 69009 | 3836 | 1080764 | Italian | |
| Quin69 | 1186941750 | 174270 | 36742 | 6616 | 538532 | English | |
| Rainbow6 | 1031011170 | 82380 | 135471 | 11535 | 1501197 | English | |
| Rakin | 842581305 | 144510 | 51854 | 5333 | 1258173 | Portuguese | |
| RatedEpicz | 582401145 | 175920 | 11860 | 3336 | 134757 | English | |
| RATIRL | 649761570 | 145050 | 14480 | 4420 | 423002 | English | |
| RebirthzTV | 548041425 | 118920 | 14578 | 4629 | 276694 | Thai | |
| Reborn_Live | 578122875 | 32100 | 57849 | 17488 | 697007 | Spanish | |
| riotgames | 2674646715 | 80820 | 639375 | 20960 | 4487489 | English | |
| RiotGamesBrazil | 1228613130 | 38370 | 255542 | 25918 | 1011924 | Portuguese | |
| RocketLeague | 1322448480 | 33540 | 206681 | 36086 | 1409120 | English | |
| ROSHTEIN | 1435735725 | 118995 | 45843 | 11717 | 381918 | English | |
| Rubius | 2588632635 | 58275 | 240096 | 42948 | 5751354 | Spanish | |
| Sacriel | 1002681105 | 163095 | 66781 | 5573 | 672403 | English | |
| saddummy | 1241997345 | 182310 | 25482 | 6681 | 580794 | Korean | |
| Sardoche | 1361024835 | 164235 | 144066 | 8066 | 746865 | French | |
| Scarra | 864157695 | 138360 | 27421 | 6060 | 1242014 | English | |
| Sfory | 612617325 | 73590 | 75219 | 3197 | 457502 | Russian | |
| Shlorox | 625142130 | 115650 | 13945 | 5216 | 331632 | German | |
| shroud | 888505170 | 30240 | 471281 | 29612 | 7744066 | English | |
| shuteye_orange | 728551080 | 325935 | 7441 | 2217 | 85247 | Chinese | |
| SilverName | 1006608690 | 95625 | 29927 | 10618 | 614395 | Russian | |
| SkipNhO | 553663800 | 131580 | 23408 | 3875 | 1076499 | Portuguese | |
| SkipNhOLIVE | 600910875 | 498765 | 7940 | 1196 | 324765 | Portuguese | |
| SmiteGame | 586925850 | 344055 | 33245 | 1588 | 535211 | English | |
| sneakylol | 1149209820 | 174885 | 22759 | 6775 | 1659108 | English | |
| sodapoppin | 2329440420 | 115305 | 107833 | 19659 | 2786162 | English | |
| Solary | 1546597380 | 486510 | 24470 | 3187 | 493207 | French | |
| SolaryFortnite | 1223349555 | 381735 | 46710 | 3180 | 1478270 | French | |
| SolaryHS | 827452485 | 460065 | 17513 | 1802 | 149073 | French | |
| Squeezie | 667977780 | 29775 | 158972 | 19260 | 2149306 | French | |
| StarLadder_cs_en | 1088832810 | 32880 | 329195 | 29956 | 820675 | English | |
| StarLadder5 | 580541850 | 41715 | 189859 | 13089 | 1029203 | Russian | |
| Stray228 | 972961650 | 93750 | 36971 | 10290 | 773712 | Russian | |
| stylishnoob4 | 1029543660 | 118515 | 25263 | 8485 | 354579 | Japanese | |
| summit1g | 6091677300 | 211845 | 310998 | 25610 | 5310163 | English | |
| Swagg | 790021440 | 108375 | 74199 | 6309 | 784966 | English | |
| Symfuhny | 1076179485 | 137400 | 45671 | 7327 | 2355063 | English | |
| SypherPK | 1016450160 | 145230 | 130401 | 6553 | 3611359 | English | |
| TeePee | 789698115 | 170010 | 78741 | 4410 | 520519 | English | |
| TFBlade | 1394312895 | 141285 | 35833 | 9117 | 1008040 | English | |
| Tfue | 3671000070 | 123660 | 285644 | 29602 | 8938903 | English | |
| TheGrefg | 1757406750 | 54855 | 538444 | 28887 | 3795667 | Spanish | |
| TheRealKnossi | 1811696100 | 56010 | 288459 | 24595 | 1260160 | German | |
| Thijs | 794621265 | 108720 | 24923 | 7180 | 755116 | English | |
| TimTheTatman | 2834436990 | 108780 | 142067 | 25664 | 5265659 | English | |
| tmxk319 | 857951685 | 116400 | 71933 | 7173 | 427926 | Korean | |
| Trainwreckstv | 1021699920 | 148425 | 49379 | 7134 | 728097 | English | |
| Trymacs | 1184154975 | 107880 | 50957 | 10735 | 1607134 | German | |
| UberHaxorNova | 615472275 | 181950 | 7808 | 3247 | 421256 | English | |
| uzra | 812362125 | 208785 | 14181 | 3683 | 185506 | Chinese | |
| Vader | 1110952500 | 184305 | 16289 | 5913 | 424374 | English | |
| Vinesauce | 536989080 | 86790 | 19065 | 6125 | 442493 | English | |
| WePlayEsport_EN | 704823000 | 107745 | 87751 | 6127 | 175061 | English | |
| WePlayEsport_RU | 853324635 | 92970 | 115737 | 8627 | 346934 | Russian | |
| woowakgood | 650364705 | 158850 | 14177 | 4100 | 591500 | Korean | |
| wtcN | 582125625 | 77385 | 73861 | 7438 | 1852272 | Turkish | |
| x2Twins | 595707975 | 125745 | 31874 | 4167 | 1288969 | English | |
| Xayoo_ | 575138175 | 91260 | 23935 | 6091 | 572789 | Polish | |
| xQcOW | 6196161750 | 215250 | 222720 | 27716 | 3246298 | English | |
| Yassuo | 1347412425 | 103905 | 70587 | 10531 | 1878416 | English | |
| ybicanoooobov | 938816460 | 114765 | 17036 | 8255 | 523512 | Russian | |
| YoDa | 1690237110 | 135675 | 123796 | 12868 | 1792625 | Portuguese | |
| Yogscast | 644580630 | 443130 | 54885 | 1384 | 961860 | English | |
| ZanoXVII | 610609920 | 95730 | 30648 | 6073 | 514866 | Italian | |
| ZeratoR | 1011013035 | 84960 | 262273 | 10516 | 880728 | French | |
| zilioner | 601906185 | 97545 | 43164 | 5843 | 465887 | Korean |
Symbols
| Channel | Watch Time | Stream Time | Peak Viewers | Average Viewers | Followers | Language | |
| aceu | 744620970 | 118125 | 26141 | 6328 | 859439 | English | |
| AdmiralBahroo | 1188645990 | 141210 | 21053 | 8152 | 778055 | English | |
| AdmiralBulldog | 972317520 | 154845 | 16681 | 6198 | 694253 | English | |
| Agraelus | 779867430 | 169515 | 23555 | 4642 | 414951 | Czech | |
| alanzoka | 2055003870 | 103770 | 89153 | 19560 | 3445134 | Portuguese | |
| allkeyshop_tv | 663185955 | 487005 | 6075 | 1361 | 67472 | English | |
| Amouranth | 618067800 | 235170 | 13495 | 2560 | 1707804 | English | |
| Anomaly | 2865429915 | 92880 | 125408 | 12377 | 2607076 | English | |
| Anton | 581034300 | 142890 | 137531 | 3199 | 207484 | English | |
| Asmongold | 3668799075 | 82260 | 263720 | 42414 | 1563438 | English | |
| auronplay | 2410022550 | 40575 | 170115 | 53986 | 3983847 | Spanish | |
| Baiano | 859718520 | 85860 | 107069 | 9229 | 425797 | Portuguese | |
| benjyfishy | 580787955 | 34350 | 75491 | 14423 | 1739112 | English | |
| BeyondTheSummit | 1339097490 | 505080 | 116547 | 2635 | 923689 | English | |
| BLASTPremier | 753808200 | 25260 | 113167 | 24689 | 501371 | English | |
| blusewilly_retry | 618755280 | 100515 | 17585 | 5941 | 374480 | Chinese | |
| BobRoss | 558170835 | 238035 | 11659 | 2175 | 1519266 | English | |
| bratishkinoff | 646333065 | 60795 | 56790 | 10626 | 1128907 | Russian | |
| Brunenger | 559915035 | 183225 | 64308 | 2844 | 659652 | Spanish | |
| Bugha | 1324519320 | 100470 | 66311 | 12982 | 2942212 | English | |
| buster | 884353800 | 59295 | 97838 | 14195 | 1087377 | Russian | |
| C_a_k_e | 588662010 | 129660 | 17317 | 4403 | 302633 | Russian | |
| CasinoDaddy | 577240710 | 267465 | 6524 | 2168 | 151098 | English | |
| Castro_1021 | 1845157080 | 100215 | 125133 | 17779 | 2411995 | English | |
| CDNThe3rd | 722562675 | 134280 | 55752 | 4919 | 2009972 | English | |
| Cellbit | 817373955 | 103095 | 68813 | 8264 | 1293451 | Portuguese | |
| Chap | 550951215 | 157545 | 22571 | 3430 | 1272899 | English | |
| chocoTaco | 620395515 | 160830 | 30514 | 3846 | 1134153 | English | |
| ClassyBeef | 558883590 | 273660 | 33624 | 1941 | 108623 | English | |
| Clix | 1256647110 | 89760 | 81926 | 12996 | 2035180 | English | |
| cloakzy | 748023225 | 101670 | 40497 | 6743 | 2138294 | English | |
| CohhCarnage | 2029212570 | 175230 | 43615 | 11343 | 1264808 | English | |
| coscu | 622424175 | 77160 | 80444 | 8919 | 1865296 | Spanish | |
| CriticalRole | 539495145 | 21300 | 110800 | 17689 | 571210 | English | |
| csgomc_ru | 1308967860 | 77955 | 364816 | 17020 | 492954 | Russian | |
| CSRuHub | 540556545 | 110880 | 106003 | 5044 | 511431 | Russian | |
| dakotaz | 978947160 | 132615 | 43397 | 7112 | 4520305 | English | |
| DansGaming | 653181210 | 187530 | 33646 | 3270 | 817365 | English | |
| dasMEHDI | 1172969025 | 231465 | 47683 | 5013 | 299048 | English | |
| ddahyoni | 711864630 | 152445 | 17253 | 4534 | 322895 | Korean | |
| Destiny | 650910525 | 162690 | 24101 | 3894 | 571183 | English | |
| Diegosaurs | 558587535 | 128580 | 32463 | 4150 | 521201 | English | |
| dogdog | 622199835 | 103335 | 24727 | 5856 | 594239 | English | |
| Domingo | 662502810 | 65610 | 102022 | 11423 | 829700 | French | |
| dota2mc_ru | 1464683175 | 66675 | 182869 | 19495 | 428284 | Russian | |
| Dota2RuHub | 1330625430 | 92160 | 105359 | 13189 | 777510 | Russian | |
| dota2ti | 1017577605 | 6315 | 483530 | 147643 | 663297 | English | |
| dota2ti_ru | 812538090 | 6195 | 457060 | 126232 | 541644 | Russian | |
| DrDisrespect | 1839882465 | 73065 | 97540 | 23794 | 4450718 | English | |
| DreadzTV | 715644660 | 109725 | 35865 | 6249 | 675908 | Russian | |
| DreamHackCS | 1052904720 | 314595 | 212201 | 5001 | 1801697 | English | |
| DrLupo | 1517612010 | 172350 | 90696 | 8311 | 4115083 | English | |
| Elajjaz | 726000045 | 145755 | 13080 | 4922 | 346566 | English | |
| elded | 853049385 | 110940 | 44758 | 7699 | 2601858 | Spanish | |
| ElmiilloR | 686456910 | 126105 | 45726 | 5163 | 426716 | Spanish | |
| Elraenn | 726379485 | 51150 | 65543 | 13224 | 1223076 | Turkish | |
| EsfandTV | 888938940 | 189045 | 29597 | 4393 | 471970 | English | |
| ESL_CSGO | 3970318140 | 517740 | 300575 | 7714 | 3944850 | English | |
| ESL_DOTA2 | 661049190 | 212010 | 99858 | 4714 | 337177 | English | |
| Evelone192 | 1474742220 | 83010 | 106900 | 16422 | 1075101 | Russian | |
| Fextralife | 3301867485 | 147885 | 68795 | 18985 | 508816 | English | |
| forsen | 1106781045 | 109140 | 33966 | 10080 | 1308165 | English | |
| fps_shaka | 1131509385 | 215160 | 26572 | 5195 | 303671 | Japanese | |
| Fresh | 1464179820 | 147660 | 57431 | 9728 | 3135667 | English | |
| GamesDoneQuick | 1619144100 | 87450 | 234826 | 6734 | 1724316 | English | |
| Gaules | 5644590915 | 515280 | 387315 | 10976 | 1767635 | Portuguese | |
| Giantwaffle | 612594165 | 165525 | 36340 | 3429 | 878934 | English | |
| Gladd | 543954570 | 171195 | 54477 | 2779 | 337972 | English | |
| GMHikaru | 554249955 | 68355 | 46106 | 7155 | 505361 | English | |
| godjj | 544706325 | 126705 | 12461 | 4378 | 331744 | Chinese | |
| Gorgc | 1252711830 | 141135 | 56449 | 8683 | 391726 | English | |
| Gotaga | 1538511315 | 141675 | 81644 | 10750 | 2401580 | French | |
| Greekgodx | 561616335 | 89745 | 45741 | 7083 | 1278824 | English | |
| GRONKH | 1017544335 | 54645 | 100330 | 17860 | 1216020 | German | |
| H2P_Gucio | 575998575 | 157995 | 10011 | 3581 | 220488 | Polish | |
| handongsuk | 1621667925 | 127815 | 44976 | 12869 | 385250 | Korean | |
| hanryang1125 | 2186662470 | 181230 | 26999 | 12201 | 494445 | Korean | |
| HasanAbi | 1339344945 | 193560 | 44649 | 6543 | 470123 | English | |
| ibai | 1412913285 | 57795 | 173238 | 22837 | 1894953 | Spanish | |
| imaqtpie | 596368095 | 167190 | 26087 | 3478 | 2652018 | English | |
| IzakOOO | 717096330 | 129165 | 43050 | 4463 | 1461767 | Polish | |
| Jahrein | 566176425 | 61890 | 43683 | 8929 | 1422862 | Turkish | |
| Jinnytty | 569601090 | 151815 | 15190 | 3607 | 372334 | English | |
| jinu6734 | 597275955 | 121545 | 12810 | 4710 | 274875 | Korean | |
| JLTomy | 1228169940 | 121455 | 42079 | 10053 | 428073 | French | |
| jovirone | 553283745 | 96450 | 29272 | 5632 | 1089830 | Portuguese | |
| juansguarnizo | 849083325 | 123780 | 45631 | 6039 | 1204773 | Spanish | |
| jukes | 628079220 | 76605 | 24263 | 8165 | 1327059 | Portuguese | |
| Kamet0 | 600882645 | 130620 | 70983 | 4560 | 565661 | French | |
| KendineMuzisyen | 567374295 | 58545 | 43422 | 9583 | 1372290 | Turkish | |
| kimdoe | 549244755 | 134985 | 11759 | 4062 | 287639 | Korean | |
| Kitboga | 656365305 | 80760 | 20913 | 7394 | 772055 | English | |
| Klean | 545108145 | 169605 | 101124 | 2801 | 276242 | English | |
| LCK | 1351758525 | 37140 | 171861 | 36030 | 934688 | English | |
| LCK_Korea | 1916365860 | 47325 | 140557 | 39848 | 619382 | Korean | |
| LCS | 1461310140 | 31125 | 214124 | 46459 | 1162746 | English | |
| LEC | 1470431925 | 45660 | 305119 | 28830 | 973727 | English | |
| lestream | 955346835 | 253395 | 47638 | 3652 | 883706 | French | |
| LIRIK | 2832930285 | 128490 | 89170 | 21739 | 2666382 | English | |
| Locklear | 619247415 | 108450 | 50103 | 5512 | 824676 | French | |
| lol_ambition | 639445965 | 113415 | 57254 | 5332 | 362297 | Korean | |
| lol_woolf | 532969650 | 50910 | 73800 | 9633 | 308528 | Korean | |
| loltyler1 | 2928356940 | 122490 | 89387 | 22381 | 3530767 | English | |
| Lord_Kebun | 1943299035 | 153720 | 34830 | 12367 | 434200 | English | |
| LPL | 850636305 | 48765 | 146577 | 17573 | 502467 | English | |
| LVNDMARK | 788421150 | 199350 | 96236 | 3688 | 248829 | English | |
| LVPes | 1115650275 | 90960 | 233009 | 12947 | 587677 | Spanish | |
| Mathil1 | 561997440 | 134715 | 25866 | 3953 | 293595 | English | |
| Maximilian_DOOD | 1023316710 | 110040 | 43253 | 9235 | 833047 | English | |
| Method | 905107560 | 230940 | 148350 | 4135 | 401400 | English | |
| Mithrain | 530456265 | 105540 | 40183 | 4990 | 1258713 | Turkish | |
| Mizkif | 1052047935 | 123120 | 32671 | 7899 | 591653 | English | |
| mobilmobil | 652685055 | 126120 | 16497 | 5303 | 446426 | Chinese | |
| MontanaBlack88 | 2408460990 | 67740 | 181600 | 33514 | 2911316 | German | |
| MOONMOON | 1527882945 | 124680 | 24892 | 11220 | 923448 | English | |
| muse_tw | 625892895 | 446655 | 16702 | 1397 | 121053 | Chinese | |
| MYM_ALKAPONE | 599850495 | 97830 | 26221 | 5948 | 770535 | Spanish | |
| Myth | 1479214575 | 134760 | 122552 | 9396 | 6726893 | English | |
| Nick28T | 556741020 | 183660 | 15155 | 3024 | 977377 | English | |
| NickEh30 | 1148114400 | 117885 | 85073 | 9702 | 1660204 | English | |
| NICKMERCS | 3360675195 | 136275 | 115633 | 24181 | 4074287 | English | |
| Nightblue3 | 899215845 | 118980 | 17738 | 7234 | 2641880 | English | |
| nl_Kripp | 1470897720 | 155895 | 29316 | 9256 | 1379123 | English | |
| NOBRU | 888211260 | 38655 | 132224 | 22070 | 1549722 | Portuguese | |
| nokduro | 555637890 | 140670 | 15858 | 4040 | 165823 | Korean | |
| NoWay4u_Sir | 1234567245 | 139920 | 24286 | 8479 | 383892 | German | |
| OgamingLoL | 1483207890 | 496950 | 204491 | 3020 | 523758 | French | |
| ONSCREEN | 1197130335 | 134880 | 88516 | 4134 | 918654 | English | |
| OverwatchLeague | 805163370 | 24480 | 254493 | 33132 | 1796619 | English | |
| Papaplatte | 1105525440 | 125550 | 42230 | 8546 | 919026 | German | |
| Pestily | 1659741015 | 138300 | 168112 | 8481 | 616168 | English | |
| PlayHearthstone | 661075170 | 41175 | 43877 | 13154 | 825727 | English | |
| pokimane | 964334055 | 56505 | 112160 | 16026 | 5367605 | English | |
| POW3Rtv | 721548885 | 177885 | 69009 | 3836 | 1080764 | Italian | |
| Quin69 | 1186941750 | 174270 | 36742 | 6616 | 538532 | English | |
| Rainbow6 | 1031011170 | 82380 | 135471 | 11535 | 1501197 | English | |
| Rakin | 842581305 | 144510 | 51854 | 5333 | 1258173 | Portuguese | |
| RatedEpicz | 582401145 | 175920 | 11860 | 3336 | 134757 | English | |
| RATIRL | 649761570 | 145050 | 14480 | 4420 | 423002 | English | |
| RebirthzTV | 548041425 | 118920 | 14578 | 4629 | 276694 | Thai | |
| Reborn_Live | 578122875 | 32100 | 57849 | 17488 | 697007 | Spanish | |
| riotgames | 2674646715 | 80820 | 639375 | 20960 | 4487489 | English | |
| RiotGamesBrazil | 1228613130 | 38370 | 255542 | 25918 | 1011924 | Portuguese | |
| RocketLeague | 1322448480 | 33540 | 206681 | 36086 | 1409120 | English | |
| ROSHTEIN | 1435735725 | 118995 | 45843 | 11717 | 381918 | English | |
| Rubius | 2588632635 | 58275 | 240096 | 42948 | 5751354 | Spanish | |
| Sacriel | 1002681105 | 163095 | 66781 | 5573 | 672403 | English | |
| saddummy | 1241997345 | 182310 | 25482 | 6681 | 580794 | Korean | |
| Sardoche | 1361024835 | 164235 | 144066 | 8066 | 746865 | French | |
| Scarra | 864157695 | 138360 | 27421 | 6060 | 1242014 | English | |
| Sfory | 612617325 | 73590 | 75219 | 3197 | 457502 | Russian | |
| Shlorox | 625142130 | 115650 | 13945 | 5216 | 331632 | German | |
| shroud | 888505170 | 30240 | 471281 | 29612 | 7744066 | English | |
| shuteye_orange | 728551080 | 325935 | 7441 | 2217 | 85247 | Chinese | |
| SilverName | 1006608690 | 95625 | 29927 | 10618 | 614395 | Russian | |
| SkipNhO | 553663800 | 131580 | 23408 | 3875 | 1076499 | Portuguese | |
| SkipNhOLIVE | 600910875 | 498765 | 7940 | 1196 | 324765 | Portuguese | |
| SmiteGame | 586925850 | 344055 | 33245 | 1588 | 535211 | English | |
| sneakylol | 1149209820 | 174885 | 22759 | 6775 | 1659108 | English | |
| sodapoppin | 2329440420 | 115305 | 107833 | 19659 | 2786162 | English | |
| Solary | 1546597380 | 486510 | 24470 | 3187 | 493207 | French | |
| SolaryFortnite | 1223349555 | 381735 | 46710 | 3180 | 1478270 | French | |
| SolaryHS | 827452485 | 460065 | 17513 | 1802 | 149073 | French | |
| Squeezie | 667977780 | 29775 | 158972 | 19260 | 2149306 | French | |
| StarLadder_cs_en | 1088832810 | 32880 | 329195 | 29956 | 820675 | English | |
| StarLadder5 | 580541850 | 41715 | 189859 | 13089 | 1029203 | Russian | |
| Stray228 | 972961650 | 93750 | 36971 | 10290 | 773712 | Russian | |
| stylishnoob4 | 1029543660 | 118515 | 25263 | 8485 | 354579 | Japanese | |
| summit1g | 6091677300 | 211845 | 310998 | 25610 | 5310163 | English | |
| Swagg | 790021440 | 108375 | 74199 | 6309 | 784966 | English | |
| Symfuhny | 1076179485 | 137400 | 45671 | 7327 | 2355063 | English | |
| SypherPK | 1016450160 | 145230 | 130401 | 6553 | 3611359 | English | |
| TeePee | 789698115 | 170010 | 78741 | 4410 | 520519 | English | |
| TFBlade | 1394312895 | 141285 | 35833 | 9117 | 1008040 | English | |
| Tfue | 3671000070 | 123660 | 285644 | 29602 | 8938903 | English | |
| TheGrefg | 1757406750 | 54855 | 538444 | 28887 | 3795667 | Spanish | |
| TheRealKnossi | 1811696100 | 56010 | 288459 | 24595 | 1260160 | German | |
| Thijs | 794621265 | 108720 | 24923 | 7180 | 755116 | English | |
| TimTheTatman | 2834436990 | 108780 | 142067 | 25664 | 5265659 | English | |
| tmxk319 | 857951685 | 116400 | 71933 | 7173 | 427926 | Korean | |
| Trainwreckstv | 1021699920 | 148425 | 49379 | 7134 | 728097 | English | |
| Trymacs | 1184154975 | 107880 | 50957 | 10735 | 1607134 | German | |
| UberHaxorNova | 615472275 | 181950 | 7808 | 3247 | 421256 | English | |
| uzra | 812362125 | 208785 | 14181 | 3683 | 185506 | Chinese | |
| Vader | 1110952500 | 184305 | 16289 | 5913 | 424374 | English | |
| Vinesauce | 536989080 | 86790 | 19065 | 6125 | 442493 | English | |
| WePlayEsport_EN | 704823000 | 107745 | 87751 | 6127 | 175061 | English | |
| WePlayEsport_RU | 853324635 | 92970 | 115737 | 8627 | 346934 | Russian | |
| woowakgood | 650364705 | 158850 | 14177 | 4100 | 591500 | Korean | |
| wtcN | 582125625 | 77385 | 73861 | 7438 | 1852272 | Turkish | |
| x2Twins | 595707975 | 125745 | 31874 | 4167 | 1288969 | English | |
| Xayoo_ | 575138175 | 91260 | 23935 | 6091 | 572789 | Polish | |
| xQcOW | 6196161750 | 215250 | 222720 | 27716 | 3246298 | English | |
| Yassuo | 1347412425 | 103905 | 70587 | 10531 | 1878416 | English | |
| ybicanoooobov | 938816460 | 114765 | 17036 | 8255 | 523512 | Russian | |
| YoDa | 1690237110 | 135675 | 123796 | 12868 | 1792625 | Portuguese | |
| Yogscast | 644580630 | 443130 | 54885 | 1384 | 961860 | English | |
| ZanoXVII | 610609920 | 95730 | 30648 | 6073 | 514866 | Italian | |
| ZeratoR | 1011013035 | 84960 | 262273 | 10516 | 880728 | French | |
| zilioner | 601906185 | 97545 | 43164 | 5843 | 465887 | Korean |
Project 7 instructions.docx
1. Paycheck Calculator
You work in the human resources department of your company helping new employees fill out the necessary paperwork to get their first paycheck. There are a number of decisions that employees must make when they complete this paperwork, including (1) which health insurance package to buy (this impacts how much money will be deducted each pay period to pay for the premium), (2) how much money to put in a flexible spending account annually to cover medical and childcare expenses, and (3) the percentage of their regular paychecks to invest in the company's 401k retirement savings plan. The expenses paid for these three items are not subject to Federal or State income taxes.
New employees must also complete a W4 form where they claim their marital status and number of allowances for deductions for tax purposes. Often these employees will ask you to project their first paycheck so that they know how much money to plan on for their personal budget.
You have decided to create a spreadsheet model that will make these projections given the information the new employee provides on the paperwork.
Note: Because the employee information will change as you work with different employees, your solution should be designed to handle changing employee information. For example, the current employee has worked overtime (the employee has worked 85 hours, and a regular work period has 80 hours). Your solution should calculate this employee's pay information and correctly handle an employee who does not work overtime.
1.1
Enter the hours worked and the pay rate for the employee into the Paycheck Calculations section of the model.
a. Reference the hours worked (C3) and pay rate (C4) values in the "Employee Information" area of the spreadsheet model.
1.2
Calculate the regular pay.
a. Reference cell C21 for the "Hours Worked".
b. The regular pay will be the hours worked times the pay rate unless the employee works overtime (more than the number of regular hours in the pay period - described in the model assumptions).
c. If the employee works overtime, the regular pay is the rate times the number regular hours in the pay period.
1.3
Calculate the overtime pay.
a. Reference cell C21 for the "Hours Worked".
b. The employee is paid 1.5 times the regular pay rate for any time the employee works more than the number regular hours in pay period.
1.4
Calculate the total gross pay.
· The total gross pay is the sum of the regular pay and the overtime pay.
1.5
Reference the health insurance deduction.
a. Reference the appropriate cell in the employee information section of the model for the health insurance deduction.
b. Notice the amount in this section is already calculated for each paycheck.
1.6
Calculate the flexible spending deduction.
a. Reference the appropriate cell in the employee information section of the model.
b. Notice that the flexible spending deduction is an annual rate.
c. You will need to divide this by the number of paychecks per year in the Model Assumptions section of the model.
1.7
Calculate the retirement savings deduction.
· The retirement savings deduction is the total gross pay times the retirement savings percentage for the employee.
1.8
Calculate the total deductions.
· The total deductions equals the sum of the insurance, flexible spending, and retirement savings deductions.
1.9
Calculate the adjusted income.
· The adjusted income is the difference between the total gross pay and the total deductions.
1.10
Use an IF function to calculate the Federal Income Tax Rate.
a. The federal tax rate is a function of the adjusted income and the employee's marital status.
b. Reference the tax tables in the Model Assumptions and the marital status in the Employee Information section to construct a formula (or set of nested formulas) to calculate the tax rate.
c. For example, a single employee who earned $15,000 would pay a 33% marginal tax rate.
1.11
Calculate the federal income tax.
a. The federal income tax is the adjusted income times the federal income tax rate calculated in cell C36.
1.12
Calculate the tax adjustment for allowances. An employee will have less federal income tax withheld for every allowance that they claim. Allowances account for the number of dependents they will claim on their taxes and other factors that will ultimately reduce the amount of federal taxes they will pay.
a. The adjustment for allowances is calculated as the product of the number of allowances (in the employee information section), the allowance deduction amount (in the model assumptions), and the employee Federal Income Tax rate (calculated in task 10).
1.13
Calculate the net federal income tax.
a. The net federal income tax is the difference between the federal income tax and the adjustment for allowances.
b. If the adjustment for allowances is greater than the federal income tax, then the net federal income tax is zero (0).
1.14
Calculate the state income tax.
· The state tax is the adjusted income times the state tax rate in the Model Assumptions section.
1.15
Calculate the medicare tax rate.
· The medicare tax rate is the total gross pay times the medicare tax rate in the Model Assumptions section.
1.16
Calculate the social security tax.
· The social security tax is the total gross pay times the social security tax rate in the Model Assumptions section.
1.17
Calculate the total taxes.
· The total taxes is the sum of the net federal income tax, the state income tax, the medicare tax, and social security tax.
2. Phone Plan Analysis
You are planning to switch your cell phone provider. You have imported your data usage from the last six months with your previous provider and entered this information on the "Data" worksheet so that you can analyze which plan is best for you based on your prior data usage.
The Cellular worksheet presents two options for a cell phone plan with the new company. You could choose a pay-as-you-go plan or an unlimited plan. You want to evaluate which plan will be best for you. Both plans provide unlimited calling and texting. The unlimited plan also offers unlimited data usage. The pay-as-you-go plan includes 12 gigabytes of data. Any data you use beyond the 12 gigabytes will cost $15 per gigabyte.
Complete the tasks to compare what your bill would be for both plan options based on the past data usage.
2.1
Complete the Megabytes Used row (G4:L4) of the Usage Summary table on the Data worksheet.
a. Build the formula to calculate total Megabytes Used for January (G4), by referencing the month name in G3 and the appropriate columns in the data table (B3:D183).
b. Reuse your formula to calculate the Megabytes Used for the other months.
c. Notice that the "Gigabytes Used" (G5:L5) row is already completed in the worksheet. "Gigabytes Used" is calculated as the "Megabytes Used" divided by 1024 (the number of megabytes in a gigabyte).
d. The Gigabytes Used (Rounded) (G6:L6) row is also completed in the worksheet. Since the mobile carrier rounds the Gigabytes Used up to the next whole gigabyte, the ROUNDUP function is used to adjust the Gigabytes Used up to the next integer.
2.2
Complete the Monthly Charges row (F5:K5) of the Cost Comparison table on the Cellular worksheet for Option 1.
a. Enter the monthly charges for January (F5) by referenceing the monthly charges for option 1 (C4) in the Plan Options table.
b. Reuse your formula to complete the monthly charges for Option 1 for each month.
2.3
Complete the Taxes and Fees row (F6:K6) of the Cost Comparison table on the Cellular worksheet for Option 1.
a. Calculate the tax for January in cell F6.
b. The taxes and fees are calculated as the monthly charges (not including any data charges) times the taxes and fees rate (cell C12) on the Plan Options table.
c. Reuse your formula to complete the taxes and fees for Option 1 for each month.
2.4
Complete the Gigabytes Used (Rounded) row (F7:K7) of the Cost Comparison table on the Cellular worksheet for Option 1. The phone carrier rounds the data used up to the nearest gigabyte for billing purposes.
a. Enter the rounded number of gigabytes used for January in cell F7 by referencing its calculated value in the appropriate cell on the Data sheet.
b. Reuse your formula to complete the Gigabytes Used (Rounded) for Option 1 for the remaining months.
2.5
Complete the Data Charges row (F8:K8) of the Cost Comparison table on the Cellular worksheet for Option 1.
a. Calculate the data charges for option 1 in January in cell F8.
b. The phone carrier charges $15 for each gigabyte used (rounded up to the next whole gigabyte) that exceeds the data amount included with the plan.
c. Be sure to reference the Gigabytes Used (Rounded) in January as well as Data Charges per Gigabyte and Gigabytes Included from the Plan Options section of the worksheet.
d. Display a zero if the data does not exceed the gigabytes included with the plan.
e. Reuse your formula to complete the data charges for Option 1 for each month.
2.6
Complete the Total Cost row (F9:K9) of the Cost Comparison table on the Cellular worksheet for Option 1.
a. Calculate the total cost for option 1 in January in cell F9.
b. The total cost is the sum of the monthly charges, taxes and fees, and the data charges for January.
c. Reuse your formula to complete the total cost for Option 1 for each month.
2.7
Complete the Which is best? row (F17:L17) of the Cost Comparison table on the Cellular worksheet.
a. Notice that the costs associated with Option 2 are already calculated in the worksheet.
b. Write a formula to display "Option 1" if the total cost of Option 1 is less than the total cost of Option 2 for that month.
c. If the total cost of Option 2 is less than the total cost of Option 1, display "Option 2".
d. Otherwise, display "No Difference".
e. Reuse your formula to determine which option is best for each month and in cell L17 to determine which option is best overall.
3. Data Validation Assessment
RideShare USA is private taxi service that provides scheduled rides for customers from the airport to any location within 100 miles. The form on the RideShare worksheet is a spreadsheet model used to quote pricing to customers. A salesperson enters information about the trip and the model calculates the price. The cost of the trip is determined by the number of travelers and the miles traveled. RideShare USA charges a premium for peak times of the day. A deposit is required for trips with more than 4 travelers. Complete the tasks by using data validation to constrain the trip information entries to help ensure that valid data is entered.
3.1
Use data validation in cell C3 to constrain entries for the customer name.
a. Allow text with a length of at most (less than or equal to) 30 characters.
b. Configure the input message with a title of "Customer Name" and "Enter the customer name." as the message.
c. Set the error alert with the title of "Invalid Customer Name", and "The customer name contains more than 30 characters." as the message.
3.2
Use data validation in cell C4 to constrain entries for the trip date.
a. Allow a date occuring on or after (greater than or equal to) 1/1/2021.
b. Configure the input message with a title of "Trip Date" and "Enter the trip date." as the message.
c. Set the error alert with the title of "Invalid Trip Date", and "The trip date must occur on or after 1/1/2021." as the message.
3.3
Use data validation in cell C5 to constrain entries for the time of the trip.
a. Allow a time occuring during normal business hours (6:00 AM to 11:00 PM).
b. Configure the input message with a title of "Time of the Trip" and "Enter the time of the trip." as the message.
c. Set the error alert with the title of "Invalid Trip Time", and "The trip must occur during normal business hours (6:00 AM to 11:00 PM)." as the message.
3.4
Use data validation in cell C6 to constrain entries for the number of travelers.
a. Allow a whole number between 1 and 12.
b. Configure the input message with a title of "Number of Travelers" and "Enter the number of travelers." as the message.
c. Set the error alert with the title of "Invalid Number of Travelers", and "The number of travelers must be a whole number between 1 and 12." as the message.
3.5
Use data validation in cell C7 to constrain entries for the number of mile.
a. Have the user select the number of miles from the list of 25, 50, 75, and 100.
b. Configure the input message with a title of "Number of Miles" and "Select the number of miles." as the message.
c. Set the error alert with the title of "Invalid Number of Miles", and "The number of miles was not selected from the list." as the message.
3.6
Use data validation in cell C8 to constrain entries for whether the deposit is required.
a. Use a custom data validation rule.
b. The value for deposit required must be "Yes" if the number of travelers is more than 4, otherwise it is should be "No".
c. Hint - a valid formula for the custom rule is '=C8=IF(C6>4,"Yes","No")'
d. Configure the input message with a title of "Deposit Required" and "Enter whether the deposit is required." as the message.
e. Set the error alert with the title of "Invalid Deposit Required Entry", and "The value for deposit required is not correct given the number of travelers." as the message.
Project 7.xlsx
Data
| Month | Day | Megabytes Used | Usage Summary | ||||||||
| Jan | 1 | 839 | Month | Jan | Feb | Mar | Apr | May | Jun | ||
| Jan | 2 | 232 | Megabytes Used | ||||||||
| Jan | 3 | 821 | Gigabytes Used | 0 | 0 | 0 | 0 | 0 | 0 | ||
| Jan | 4 | 580 | Gigabytes Used (Rounded) | 0 | 0 | 0 | 0 | 0 | 0 | ||
| Jan | 5 | 835 | |||||||||
| Jan | 6 | 753 | * Note: 1 gigabyte is equal to 1024 megabytes | ||||||||
| Jan | 7 | 102 | |||||||||
| Jan | 8 | 566 | |||||||||
| Jan | 9 | 109 | |||||||||
| Jan | 10 | 733 | |||||||||
| Jan | 11 | 343 | |||||||||
| Jan | 12 | 553 | |||||||||
| Jan | 13 | 826 | |||||||||
| Jan | 14 | 451 | |||||||||
| Jan | 15 | 258 | |||||||||
| Jan | 16 | 498 | |||||||||
| Jan | 17 | 443 | |||||||||
| Jan | 18 | 245 | |||||||||
| Jan | 19 | 335 | |||||||||
| Jan | 20 | 807 | |||||||||
| Jan | 21 | 300 | |||||||||
| Jan | 22 | 781 | |||||||||
| Jan | 23 | 100 | |||||||||
| Jan | 24 | 720 | |||||||||
| Jan | 25 | 191 | |||||||||
| Jan | 26 | 399 | |||||||||
| Jan | 27 | 569 | |||||||||
| Jan | 28 | 694 | |||||||||
| Jan | 29 | 90 | |||||||||
| Jan | 30 | 617 | |||||||||
| Jan | 31 | 483 | |||||||||
| Feb | 1 | 235 | |||||||||
| Feb | 2 | 165 | |||||||||
| Feb | 3 | 217 | |||||||||
| Feb | 4 | 186 | |||||||||
| Feb | 5 | 826 | |||||||||
| Feb | 6 | 390 | |||||||||
| Feb | 7 | 776 | |||||||||
| Feb | 8 | 549 | |||||||||
| Feb | 9 | 751 | |||||||||
| Feb | 10 | 328 | |||||||||
| Feb | 11 | 372 | |||||||||
| Feb | 12 | 691 | |||||||||
| Feb | 13 | 116 | |||||||||
| Feb | 14 | 266 | |||||||||
| Feb | 15 | 417 | |||||||||
| Feb | 16 | 475 | |||||||||
| Feb | 17 | 532 | |||||||||
| Feb | 18 | 353 | |||||||||
| Feb | 19 | 84 | |||||||||
| Feb | 20 | 137 | |||||||||
| Feb | 21 | 809 | |||||||||
| Feb | 22 | 734 | |||||||||
| Feb | 23 | 617 | |||||||||
| Feb | 24 | 150 | |||||||||
| Feb | 25 | 179 | |||||||||
| Feb | 26 | 177 | |||||||||
| Feb | 27 | 715 | |||||||||
| Feb | 28 | 231 | |||||||||
| Mar | 1 | 345 | |||||||||
| Mar | 2 | 745 | |||||||||
| Mar | 3 | 515 | |||||||||
| Mar | 4 | 692 | |||||||||
| Mar | 5 | 268 | |||||||||
| Mar | 6 | 26 | |||||||||
| Mar | 7 | 80 | |||||||||
| Mar | 8 | 694 | |||||||||
| Mar | 9 | 488 | |||||||||
| Mar | 10 | 591 | |||||||||
| Mar | 11 | 823 | |||||||||
| Mar | 12 | 801 | |||||||||
| Mar | 13 | 73 | |||||||||
| Mar | 14 | 288 | |||||||||
| Mar | 15 | 159 | |||||||||
| Mar | 16 | 174 | |||||||||
| Mar | 17 | 113 | |||||||||
| Mar | 18 | 203 | |||||||||
| Mar | 19 | 120 | |||||||||
| Mar | 20 | 364 | |||||||||
| Mar | 21 | 481 | |||||||||
| Mar | 22 | 789 | |||||||||
| Mar | 23 | 524 | |||||||||
| Mar | 24 | 815 | |||||||||
| Mar | 25 | 743 | |||||||||
| Mar | 26 | 812 | |||||||||
| Mar | 27 | 641 | |||||||||
| Mar | 28 | 367 | |||||||||
| Mar | 29 | 324 | |||||||||
| Mar | 30 | 318 | |||||||||
| Mar | 31 | 219 | |||||||||
| Apr | 1 | 360 | |||||||||
| Apr | 2 | 555 | |||||||||
| Apr | 3 | 458 | |||||||||
| Apr | 4 | 376 | |||||||||
| Apr | 5 | 843 | |||||||||
| Apr | 6 | 522 | |||||||||
| Apr | 7 | 679 | |||||||||
| Apr | 8 | 140 | |||||||||
| Apr | 9 | 664 | |||||||||
| Apr | 10 | 836 | |||||||||
| Apr | 11 | 668 | |||||||||
| Apr | 12 | 235 | |||||||||
| Apr | 13 | 438 | |||||||||
| Apr | 14 | 502 | |||||||||
| Apr | 15 | 348 | |||||||||
| Apr | 16 | 660 | |||||||||
| Apr | 17 | 394 | |||||||||
| Apr | 18 | 336 | |||||||||
| Apr | 19 | 458 | |||||||||
| Apr | 20 | 802 | |||||||||
| Apr | 21 | 284 | |||||||||
| Apr | 22 | 145 | |||||||||
| Apr | 23 | 789 | |||||||||
| Apr | 24 | 20 | |||||||||
| Apr | 25 | 728 | |||||||||
| Apr | 26 | 66 | |||||||||
| Apr | 27 | 292 | |||||||||
| Apr | 28 | 264 | |||||||||
| Apr | 29 | 570 | |||||||||
| Apr | 30 | 752 | |||||||||
| May | 1 | 766 | |||||||||
| May | 2 | 187 | |||||||||
| May | 3 | 670 | |||||||||
| May | 4 | 309 | |||||||||
| May | 5 | 280 | |||||||||
| May | 6 | 543 | |||||||||
| May | 7 | 651 | |||||||||
| May | 8 | 763 | |||||||||
| May | 9 | 120 | |||||||||
| May | 10 | 586 | |||||||||
| May | 11 | 233 | |||||||||
| May | 12 | 43 | |||||||||
| May | 13 | 553 | |||||||||
| May | 14 | 848 | |||||||||
| May | 15 | 647 | |||||||||
| May | 16 | 741 | |||||||||
| May | 17 | 234 | |||||||||
| May | 18 | 334 | |||||||||
| May | 19 | 129 | |||||||||
| May | 20 | 257 | |||||||||
| May | 21 | 557 | |||||||||
| May | 22 | 197 | |||||||||
| May | 23 | 205 | |||||||||
| May | 24 | 142 | |||||||||
| May | 25 | 626 | |||||||||
| May | 26 | 104 | |||||||||
| May | 27 | 134 | |||||||||
| May | 28 | 352 | |||||||||
| May | 29 | 448 | |||||||||
| May | 30 | 561 | |||||||||
| May | 31 | 605 | |||||||||
| Jun | 1 | 201 | |||||||||
| Jun | 2 | 166 | |||||||||
| Jun | 3 | 846 | |||||||||
| Jun | 4 | 423 | |||||||||
| Jun | 5 | 611 | |||||||||
| Jun | 6 | 199 | |||||||||
| Jun | 7 | 131 | |||||||||
| Jun | 8 | 96 | |||||||||
| Jun | 9 | 245 | |||||||||
| Jun | 10 | 72 | |||||||||
| Jun | 11 | 753 | |||||||||
| Jun | 12 | 736 | |||||||||
| Jun | 13 | 333 | |||||||||
| Jun | 14 | 444 | |||||||||
| Jun | 15 | 156 | |||||||||
| Jun | 16 | 848 | |||||||||
| Jun | 17 | 816 | |||||||||
| Jun | 18 | 644 | |||||||||
| Jun | 19 | 711 | |||||||||
| Jun | 20 | 341 | |||||||||
| Jun | 21 | 826 | |||||||||
| Jun | 22 | 355 | |||||||||
| Jun | 23 | 265 | |||||||||
| Jun | 24 | 567 | |||||||||
| Jun | 25 | 736 | |||||||||
| Jun | 26 | 446 | |||||||||
| Jun | 27 | 351 | |||||||||
| Jun | 28 | 127 | |||||||||
| Jun | 29 | 665 | |||||||||
| Jun | 30 | 741 |
Cellular
| Plan Options | Cost Comparison | ||||||||||
| Option 1 - Pay-as-you-go | Option 1 - Pay-as-you-go | ||||||||||
| Monthly Charges | $40.00 | Month | Jan | Feb | Mar | Apr | May | Jun | Total | ||
| Data Charges per Gigabyte | $15.00 | Monthly Charges | |||||||||
| Gigabytes Included | 12 | Taxes and Fees | |||||||||
| Gigabytes Used (Rounded) | |||||||||||
| Option 2 - Unlimited Plan | Data Charges | ||||||||||
| Monthly Cost | $70.00 | Total Cost | $0.00 | ||||||||
| Taxes and Fees | Option 2 - Unlimited Plan | ||||||||||
| Rate | 18% | Month | Jan | Feb | Mar | Apr | May | Jun | Total | ||
| Monthly Charges | $70.00 | $70.00 | $70.00 | $70.00 | $70.00 | $70.00 | |||||
| Taxes | $12.60 | $12.60 | $12.60 | $12.60 | $12.60 | $12.60 | |||||
| Total Cost | $82.60 | $82.60 | $82.60 | $82.60 | $82.60 | $82.60 | $495.60 | ||||
| Which is best? |
RideShare
| Trip Information | Premium Time Lookup Table | |||||
| Customer Name | Time Period | Description | Premium | |||
| Date of Trip | 6:00 AM | Morning | 20% | |||
| Time of Trip | 10:00 AM | Mid-day | 0% | |||
| # of Travelers | 4:00 PM | Evening | 30% | |||
| # of Miles | 7:00 PM | Night | 10% | |||
| Deposit Required | ||||||
| Customer Quote | ||||||
| Prepared for: | 0 | |||||
| Rider Charge | $0.00 | |||||
| Milleage Charge | $0.00 | |||||
| Time Premium | ||||||
| Total Price | $0.00 | |||||
| Payments | ||||||
| Deposit | $0.00 | |||||
| Balance Due | $0.00 |
Paycheck
| Employee Information | Model Assumptions | ||||||||||
| Hours Worked | 85 | ||||||||||
| Pay Rate | $30.00 | # regular hours in Pay Period | 80 | Federal Tax Rate Tables | |||||||
| Marital Status | Married | # of paychecks per year | 26 | "Single" Tax Rate | "Married" Tax Rate | ||||||
| # of Allowances | 2 | Income | Rate | Income | Rate | ||||||
| Retirement Savings % | 5% | Payroll Tax information | $0 | 10% | $0 | 10% | |||||
| Health Insurance (every paycheck) | $125.00 | Medicare Rate | 1.45% | $725 | 15% | $1,450 | 15% | ||||
| Flexible Spending (Annual) | $1,500.00 | Social Security Rate | 4.2% | $2,945 | 25% | $5,891 | 25% | ||||
| $7,137 | 28% | $11,891 | 28% | ||||||||
| Paycheck Summary | Income Tax Information | $14,887 | 33% | $18,120 | 33% | ||||||
| Gross Pay | $0.00 | Allowance Deduction | $146 | $32,362 | 35% | $32,362 | 35% | ||||
| Total Deductions | $0.00 | State Tax Rate | 5% | ||||||||
| Adjusted Income | $0.00 | ||||||||||
| Total Taxes | $0.00 | ||||||||||
| Net Pay | $0.00 | ||||||||||
| Paycheck Calculations | |||||||||||
| Income | |||||||||||
| Hours Worked | |||||||||||
| Pay Rate | |||||||||||
| Regular Pay | |||||||||||
| Overtime Pay | |||||||||||
| Total Gross Pay | |||||||||||
| Deductions (Items not subject to income tax) | |||||||||||
| Insurance | |||||||||||
| Flexible Spending | |||||||||||
| Retirement | |||||||||||
| Total Deductions | |||||||||||
| Adjusted Income | |||||||||||
| Taxes | |||||||||||
| Federal Income Tax Rate | |||||||||||
| Federal Income Tax | |||||||||||
| Adjustment for Allowances | |||||||||||
| Net Federal Income Tax | |||||||||||
| State Income Tax | |||||||||||
| Medicare Tax | |||||||||||
| Social Security Tax | |||||||||||
| Total Taxes |
Project 8 instructions.docx
1. Body Mass Index
Body Mass Index (BMI) is a measure of the fat composition of a person's body. While having enough body fat is important to a person's health, having too much fat can cause serious health problems. BMI is calculated using a person's weight and height. Ideally, BMI should fall between 18.5 and 24.9.
The table on the BMI worksheet contains the height and weight measurements for a sample of men. Each man's BMI has also been calculated and is listed on table.
Complete the Summary Statistics table using the COUNT, AVERAGE, STDEV.S, and CI functions. Use the CORREL and FORECAST functions to examine the relationship between weight and height.
1.1
Use the COUNT function in cell H5 to calculate the number of men in the sample.
· Use C4:C203 as the argument for your function.
1.2
Use the AVERAGE function in cell H6 to calculate the average weight of the men in the sample.
1.3
Use the STDEV.S function in cell H7 to calculate the sample standard deviation of the weight of the men in the sample.
1.4
Use the CONFIDENCE.NORM function in cell H8 to calculate the 95% confidence interval for the weight of the men.
a. Use 0.05 as the alpha in your function.
b. Reference the count you calculated in cell H5 in your formula.
1.5
Use the AVERAGE function in cell I6 to calculate the average height of the men in the sample.
1.6
Use the STDEV.S function in cell I7 to calculate the sample standard deviation of the height of the men in the sample.
1.7
Use the CONFIDENCE.NORM function in cell I8 to calculate the 95% confidence interval for the height of the men.
a. Use 0.05 as the alpha in your function.
b. Reference the count you calculated in cell H5 in your formula.
1.8
Use the AVERAGE function in cell J6 to calculate the average BMI of the men in the sample.
1.9
Use the STDEV.S function in cell J7 to calculate the sample standard deviation of the BMI of the men in the sample.
1.10
Use the CONFIDENCE.NORM function in cell J8 to calculate the 95% confidence interval for the BMI of the men.
a. Use 0.05 as the alpha in your function.
b. Reference the count you calculated in cell H5 in your formula.
1.11
Use the CORREL function in cell J11 to determine if there is a statistical relationship between the height and weight of the men in the sample.
1.12
Use the FORECAST.LINEAR function in cell J15 to predict Jim's weight from his height.
a. Reference Jim's height in cell J14 as the value for x.
b. Reference the appropriate columns from the data table as the values for the known x's and known y's.
1.13
In cells J18 and J19, use the information you calculated about the confidence interval for weight to determine the lower and upper bounds for the 95% confidence interval for your prediction of Jim's weight in cell J15.
· Be sure to reference the confidence interval calculation rather than hard coding (typing in the values of) the confidence interval into your calculation.
2. Baseball
There are 30 Major League Baseball (MLB) teams. The table on the Baseball worksheet lists the 2019 and 1990 payroll and win totals for each team (notice that four teams were added after the 1990 season). Some MLB fans complain because the league does little to regulate the amount of money teams pay for salaries. These fans argue that the teams that spend the most money will win the most games. This would put teams from small markets (teams that earn less revenue) at a disadvantage. Complete the tasks to analyze if small market teams are at a disadvantage.
2.1
In cell I4, use the COUNT function to calculate the number of MLB teams in 1990.
· Reference range E4:E33 in your calculation.
2.2
In cell I6, use the COUNT function to calculate the number of MLB teams in 2019.
· Reference range D4:D33 in your calculation.
2.3
In cell I8, use the AVERAGE function to calculate the average salary for the teams in 1990.
2.4
In cell I10, use the AVERAGE function to calculate the average salary for the teams in 2019.
2.5
In cell I12, use the STDEV.P function to calculate the standard deviation for wins in 1990.
2.6
In cell I14, use the STDEV.P function to calculate the standard deviation for wins in 2019.
2.7
Based on your standard deviation calculations, were teams more similar in terms of wins in 1990 or in 2019?
a. Recall that standard deviation is a measure of how similar items in a list are (large standard deviations imply less similarity).
b. Select either 1990 or 2019 from the drop-down list in cell I16.
2.8
In cell I18, use the CORREL function to determine if there is a relationship between the number of wins a team earned in 2019 and the and the size of the team's salary.
2.9
In cell I21, use the CORREL function to determine if there is a relationship between the number of wins a team earned in 1990 and the size of the team's salary.
2.10
Is the relationship between wins and salary stronger in 1990 or in 2019?
· Select 1990 or 2019 using the drop-down list in cell I24.
2.11
In cell I27, use the FORECAST.LINEAR function to determine how many games a team could expect to win in 2019 if the team's salary was $150,000,000.
· Reference the salary amount in cell I28 and the appropriate ranges in the Major League Baseball Salaries and Wins by Team table as arguments for your function.
2.12
In cell I31, use the CONFIDENCE.NORM function to determine the 95% confidence interval for wins in 2019.
a. Use 0.05 as the alpha level in your analysis.
b. Reference the appropriate calculations for size and standard deviation as the arguments for your function.
2.13
In cell I33, calculate the upper limit for the 95% confidence interval for your win prediction for a team paying $150,000,000 in salaries in the year 2019.
· Reference your prediction in cell I27 and the confidence interval calculation in cell I31 in your formula.
2.14
In cell I35, calculate the lower limit for the 95% confidence interval for your win prediction for a team paying $150,000,000 in salaries in the year 2019.
· Reference your prediction in cell I27 and the confidence interval calculation in cell I31 in your formula.
Project 8.xlsx
Baseball
| Major League Baseball Salaries and Wins by Team | Statistical Analysis | |||||||||
| DIVISION | TEAM | 2019 PAYROLL | 1990 PAYROLL | 2019 WINS | 1990 Wins | Response | Task | |||
| AL East | Baltimore Orioles | $73,370,109 | $10,037,084 | 54 | 76 | Number of MLB Teams in 1990 | ||||
| AL East | Boston Red Sox | $229,196,106 | $20,983,333 | 84 | 88 | |||||
| AL East | New York Yankees | $223,019,037 | $20,991,318 | 103 | 67 | Number of MLB teams in 2019 | ||||
| AL East | Tampa Bay Rays | $64,178,722 | NA | 96 | NA | |||||
| AL East | Toronto Blue Jays | $111,371,067 | $18,486,834 | 67 | 86 | Average salary for the teams in 1990. | ||||
| AL Central | Chicago White Sox | $91,371,201 | $9,496,238 | 72 | 94 | |||||
| AL Central | Cleveland Indians | $107,693,747 | $15,152,000 | 93 | 77 | Average salary for the teams in 2019. | ||||
| AL Central | Detroit Tigers | $114,631,137 | $18,092,238 | 47 | 79 | |||||
| AL Central | Los Angeles Angels | $161,270,385 | $21,870,000 | 72 | 80 | Population standard deviation for wins 1990. | ||||
| AL Central | Minnesota Twins | $125,256,003 | $15,106,000 | 101 | 74 | |||||
| AL West | Kansas City Royals | $104,773,003 | $23,873,745 | 59 | 75 | Population standard deviation for wins in 2019. | ||||
| AL West | Los Angeles Dodgers | $207,000,814 | $21,618,704 | 106 | 86 | |||||
| AL West | Oakland Athletics | $93,394,531 | $19,987,501 | 97 | 103 | Were teams more similar regarding wins in 1990 or 2019? | ||||
| AL West | Seattle Mariners | $144,391,293 | $12,841,667 | 68 | 77 | |||||
| AL West | Texas Rangers | $148,538,766 | $15,104,372 | 78 | 83 | Correlation between the number of wins a team earned in 2019 and the amount teams' salary amount. | ||||
| NL East | Atlanta Braves | $143,947,963 | $13,328,334 | 97 | 65 | |||||
| NL East | Houston Astros | $168,804,925 | $18,830,000 | 107 | 75 | |||||
| NL East | New York Mets | $146,335,812 | $22,418,834 | 86 | 91 | Correlation between the number of wins a team earned in 1990 and the amount teams' salary amount. | ||||
| NL East | Philadelphia Phillies | $160,192,244 | $13,953,667 | 81 | 77 | |||||
| NL East | Washington Nationals* | $172,307,808 | $16,656,388 | 93 | 85 | |||||
| NL Central | Chicago Cubs | $221,590,085 | $14,496,000 | 84 | 77 | Is the relationship between wins and salary stronger in 1990 or 2019? | ||||
| NL Central | Cincinnati Reds | $128,391,569 | $14,769,500 | 75 | 91 | |||||
| NL Central | Milwaukee Brewers | $135,889,019 | $20,019,167 | 89 | 74 | |||||
| NL Central | Pittsburgh Pirates | $72,731,474 | $15,656,000 | 69 | 95 | How many wins could a team expect to earn in 2019 if they paid $150,000,000 in salary? | ||||
| NL Central | St. Louis Cardinals | $174,317,164 | $20,923,334 | 91 | 70 | $150,000,000 | ||||
| NL West | Arizona Diamondbacks | $118,927,905 | NA | 85 | NA | |||||
| NL West | Colorado Rockies | $157,162,629 | NA | 71 | NA | |||||
| NL West | Florida Marlins | $75,596,271 | NA | 57 | NA | What is the 95% confidence interval for wins in 2019? | ||||
| NL West | San Diego Padres | $104,254,790 | $18,588,334 | 70 | 75 | |||||
| NL West | San Francisco Giants | $178,582,126 | $20,942,333 | 77 | 85 | Upper 95% confidence interval level. | ||||
| * The Washington Nationals were the Montreal Expos in 1990 | ||||||||||
| NA = The team did not exist in 1990 | Lower 95% confidence interval level. | |||||||||
BMI
| Survey of Men's Weight and Height | Summary Statistics | ||||||||
| Subject | Weight | Height | BMI | ||||||
| 1 | 185.22 | 72.74 | 24.61 | Weight | Height | BMI | |||
| 2 | 173.09 | 69.03 | 25.53 | Count | |||||
| 3 | 173.24 | 70.21 | 24.71 | Average | |||||
| 4 | 155.81 | 68.76 | 23.17 | Standard | |||||
| 5 | 162.83 | 70.94 | 22.75 | 95% CI | |||||
| 6 | 141.69 | 67.50 | 21.86 | ||||||
| 7 | 194.85 | 76.92 | 23.15 | Correlation | |||||
| 8 | 201.81 | 70.74 | 28.35 | Correlation coefficient ---> | |||||
| 9 | 136.98 | 63.36 | 23.99 | ||||||
| 10 | 156.22 | 70.40 | 22.16 | Forecast* | |||||
| 11 | 194.71 | 70.61 | 27.46 | Jim's Height (in inches) ---> | 70 | ||||
| 12 | 125.78 | 72.47 | 16.84 | Predict Jim's Weight ---> | |||||
| 13 | 183.75 | 70.82 | 25.76 | ||||||
| 14 | 128.15 | 71.02 | 17.86 | Forecast Confidence Interval | |||||
| 15 | 202.77 | 70.00 | 29.09 | Lower Weight Bound ---> | |||||
| 16 | 154.39 | 70.06 | 22.11 | Upper Weight Bound ---> | |||||
| 17 | 139.91 | 70.35 | 19.87 | ||||||
| 18 | 200.67 | 72.92 | 26.53 | ||||||
| 19 | 206.44 | 72.77 | 27.41 | ||||||
| 20 | 250.26 | 79.44 | 27.88 | ||||||
| 21 | 153.79 | 70.09 | 22.01 | ||||||
| 22 | 219.13 | 72.38 | 29.41 | ||||||
| 23 | 181.56 | 70.93 | 25.37 | ||||||
| 24 | 166.69 | 70.58 | 23.53 | ||||||
| 25 | 207.97 | 68.89 | 30.80 | ||||||
| 26 | 119.41 | 71.43 | 16.45 | ||||||
| 27 | 207.78 | 68.94 | 30.74 | ||||||
| 28 | 168.17 | 68.88 | 24.92 | ||||||
| 29 | 178.07 | 68.66 | 26.55 | ||||||
| 30 | 187.84 | 71.56 | 25.78 | ||||||
| 31 | 199.66 | 68.38 | 30.02 | ||||||
| 32 | 208.32 | 70.92 | 29.11 | ||||||
| 33 | 208.27 | 70.83 | 29.18 | ||||||
| 34 | 205.41 | 70.76 | 28.84 | ||||||
| 35 | 188.97 | 72.65 | 25.17 | ||||||
| 36 | 181.26 | 69.16 | 26.64 | ||||||
| 37 | 206.80 | 68.23 | 31.23 | ||||||
| 38 | 166.96 | 67.23 | 25.97 | ||||||
| 39 | 163.82 | 67.22 | 25.49 | ||||||
| 40 | 174.02 | 69.59 | 25.27 | ||||||
| 41 | 226.85 | 72.43 | 30.40 | ||||||
| 42 | 141.77 | 71.42 | 19.54 | ||||||
| 43 | 150.46 | 68.61 | 22.47 | ||||||
| 44 | 173.39 | 67.14 | 27.04 | ||||||
| 45 | 158.68 | 71.04 | 22.10 | ||||||
| 46 | 164.49 | 71.33 | 22.73 | ||||||
| 47 | 167.28 | 70.22 | 23.85 | ||||||
| 48 | 178.51 | 70.50 | 25.25 | ||||||
| 49 | 152.66 | 69.22 | 22.40 | ||||||
| 50 | 181.85 | 71.58 | 24.95 | ||||||
| 51 | 161.28 | 69.34 | 23.58 | ||||||
| 52 | 158.09 | 69.71 | 22.87 | ||||||
| 53 | 313.65 | 71.33 | 43.33 | ||||||
| 54 | 217.80 | 70.53 | 30.78 | ||||||
| 55 | 182.51 | 68.47 | 27.37 | ||||||
| 56 | 201.80 | 72.66 | 26.87 | ||||||
| 57 | 168.45 | 72.60 | 22.47 | ||||||
| 58 | 166.85 | 71.28 | 23.08 | ||||||
| 59 | 132.52 | 68.86 | 19.65 | ||||||
| 60 | 155.31 | 70.22 | 22.14 | ||||||
| 61 | 178.68 | 69.36 | 26.11 | ||||||
| 62 | 157.60 | 70.27 | 22.44 | ||||||
| 63 | 144.04 | 70.66 | 20.28 | ||||||
| 64 | 140.40 | 68.52 | 21.02 | ||||||
| 65 | 161.50 | 68.78 | 24.00 | ||||||
| 66 | 177.42 | 69.22 | 26.03 | ||||||
| 67 | 193.98 | 71.86 | 26.41 | ||||||
| 68 | 127.04 | 69.27 | 18.61 | ||||||
| 69 | 161.99 | 69.32 | 23.70 | ||||||
| 70 | 154.78 | 68.37 | 23.28 | ||||||
| 71 | 205.28 | 70.73 | 28.84 | ||||||
| 72 | 137.58 | 71.10 | 19.13 | ||||||
| 73 | 180.74 | 72.26 | 24.33 | ||||||
| 74 | 156.74 | 70.88 | 21.93 | ||||||
| 75 | 159.29 | 70.21 | 22.72 | ||||||
| 76 | 163.61 | 69.95 | 23.51 | ||||||
| 77 | 135.97 | 70.44 | 19.27 | ||||||
| 78 | 194.95 | 72.85 | 25.82 | ||||||
| 79 | 187.27 | 69.75 | 27.06 | ||||||
| 80 | 167.65 | 69.91 | 24.11 | ||||||
| 81 | 145.19 | 71.26 | 20.10 | ||||||
| 82 | 175.32 | 66.72 | 27.69 | ||||||
| 83 | 145.90 | 67.11 | 22.78 | ||||||
| 84 | 147.99 | 68.94 | 21.89 | ||||||
| 85 | 158.84 | 69.90 | 22.85 | ||||||
| 86 | 133.72 | 69.57 | 19.42 | ||||||
| 87 | 324.61 | 69.06 | 47.84 | ||||||
| 88 | 116.12 | 69.11 | 17.09 | ||||||
| 89 | 166.65 | 69.82 | 24.04 | ||||||
| 90 | 133.19 | 68.40 | 20.01 | ||||||
| 91 | 166.83 | 68.90 | 24.71 | ||||||
| 92 | 234.30 | 76.64 | 28.04 | ||||||
| 93 | 209.97 | 72.10 | 28.39 | ||||||
| 94 | 192.20 | 68.68 | 28.64 | ||||||
| 95 | 177.53 | 73.56 | 23.06 | ||||||
| 96 | 160.10 | 70.15 | 22.87 | ||||||
| 97 | 162.23 | 70.76 | 22.78 | ||||||
| 98 | 142.59 | 69.73 | 20.62 | ||||||
| 99 | 172.84 | 69.67 | 25.03 | ||||||
| 100 | 173.53 | 69.95 | 24.93 | ||||||
| 101 | 203.83 | 71.75 | 27.83 | ||||||
| 102 | 176.53 | 68.75 | 26.25 | ||||||
| 103 | 154.01 | 70.43 | 21.82 | ||||||
| 104 | 195.47 | 68.38 | 29.38 | ||||||
| 105 | 178.26 | 68.66 | 26.58 | ||||||
| 106 | 195.97 | 68.63 | 29.25 | ||||||
| 107 | 136.30 | 69.51 | 19.83 | ||||||
| 108 | 136.60 | 67.73 | 20.93 | ||||||
| 109 | 183.17 | 68.76 | 27.24 | ||||||
| 110 | 139.87 | 70.10 | 20.01 | ||||||
| 111 | 166.70 | 72.11 | 22.54 | ||||||
| 112 | 182.58 | 68.19 | 27.60 | ||||||
| 113 | 346.97 | 70.85 | 48.60 | ||||||
| 114 | 201.21 | 72.89 | 26.62 | ||||||
| 115 | 155.64 | 71.99 | 21.11 | ||||||
| 116 | 169.18 | 72.66 | 22.53 | ||||||
| 117 | 127.96 | 68.82 | 19.00 | ||||||
| 118 | 130.98 | 69.95 | 18.82 | ||||||
| 119 | 175.29 | 69.07 | 25.83 | ||||||
| 120 | 153.05 | 67.19 | 23.83 | ||||||
| 121 | 184.47 | 68.74 | 27.45 | ||||||
| 122 | 174.01 | 68.18 | 26.31 | ||||||
| 123 | 137.37 | 70.35 | 19.51 | ||||||
| 124 | 155.44 | 67.00 | 24.34 | ||||||
| 125 | 143.23 | 69.73 | 20.71 | ||||||
| 126 | 151.47 | 72.66 | 20.17 | ||||||
| 127 | 198.46 | 72.83 | 26.30 | ||||||
| 128 | 146.49 | 69.95 | 21.05 | ||||||
| 129 | 156.97 | 70.23 | 22.37 | ||||||
| 130 | 162.52 | 71.20 | 22.54 | ||||||
| 131 | 193.22 | 70.42 | 27.39 | ||||||
| 132 | 170.11 | 72.57 | 22.71 | ||||||
| 133 | 199.84 | 72.45 | 26.76 | ||||||
| 134 | 173.08 | 70.48 | 24.50 | ||||||
| 135 | 179.38 | 71.00 | 25.02 | ||||||
| 136 | 161.66 | 72.11 | 21.85 | ||||||
| 137 | 200.56 | 72.15 | 27.08 | ||||||
| 138 | 203.29 | 72.20 | 27.42 | ||||||
| 139 | 178.19 | 70.41 | 25.27 | ||||||
| 140 | 196.96 | 68.51 | 29.50 | ||||||
| 141 | 182.40 | 69.17 | 26.80 | ||||||
| 142 | 156.70 | 67.78 | 23.98 | ||||||
| 143 | 128.07 | 69.55 | 18.61 | ||||||
| 144 | 155.96 | 70.22 | 22.24 | ||||||
| 145 | 189.31 | 71.03 | 26.38 | ||||||
| 146 | 155.85 | 68.62 | 23.27 | ||||||
| 147 | 171.73 | 68.73 | 25.55 | ||||||
| 148 | 157.78 | 67.42 | 24.40 | ||||||
| 149 | 135.66 | 67.42 | 20.98 | ||||||
| 150 | 187.63 | 71.06 | 26.12 | ||||||
| 151 | 165.95 | 70.15 | 23.71 | ||||||
| 152 | 175.64 | 69.60 | 25.49 | ||||||
| 153 | 148.58 | 68.59 | 22.20 | ||||||
| 154 | 207.53 | 69.64 | 30.08 | ||||||
| 155 | 191.66 | 70.32 | 27.24 | ||||||
| 156 | 190.91 | 71.29 | 26.41 | ||||||
| 157 | 212.51 | 69.90 | 30.58 | ||||||
| 158 | 217.84 | 70.41 | 30.89 | ||||||
| 159 | 131.01 | 70.30 | 18.64 | ||||||
| 160 | 155.26 | 71.03 | 21.63 | ||||||
| 161 | 152.22 | 70.05 | 21.81 | ||||||
| 162 | 133.03 | 69.35 | 19.45 | ||||||
| 163 | 136.07 | 69.45 | 19.83 | ||||||
| 164 | 157.78 | 68.47 | 23.66 | ||||||
| 165 | 206.03 | 72.93 | 27.24 | ||||||
| 166 | 190.31 | 72.94 | 25.15 | ||||||
| 167 | 255.85 | 76.08 | 31.07 | ||||||
| 168 | 181.47 | 68.81 | 26.95 | ||||||
| 169 | 176.24 | 70.59 | 24.86 | ||||||
| 170 | 160.75 | 69.04 | 23.71 | ||||||
| 171 | 164.40 | 70.74 | 23.09 | ||||||
| 172 | 180.34 | 68.65 | 26.90 | ||||||
| 173 | 173.16 | 71.74 | 23.65 | ||||||
| 174 | 194.04 | 71.30 | 26.83 | ||||||
| 175 | 122.90 | 68.82 | 18.24 | ||||||
| 176 | 181.26 | 71.09 | 25.22 | ||||||
| 177 | 129.15 | 67.55 | 19.90 | ||||||
| 178 | 167.34 | 67.43 | 25.87 | ||||||
| 179 | 211.36 | 70.25 | 30.11 | ||||||
| 180 | 218.92 | 70.25 | 31.19 | ||||||
| 181 | 200.71 | 68.00 | 30.51 | ||||||
| 182 | 122.90 | 67.80 | 18.79 | ||||||
| 183 | 124.36 | 68.25 | 18.77 | ||||||
| 184 | 179.55 | 71.17 | 24.92 | ||||||
| 185 | 197.01 | 70.89 | 27.56 | ||||||
| 186 | 174.77 | 68.90 | 25.88 | ||||||
| 187 | 169.85 | 71.18 | 23.56 | ||||||
| 188 | 208.79 | 69.33 | 30.53 | ||||||
| 189 | 143.54 | 71.09 | 19.96 | ||||||
| 190 | 153.48 | 69.72 | 22.20 | ||||||
| 191 | 157.98 | 71.12 | 21.96 | ||||||
| 192 | 152.88 | 69.41 | 22.31 | ||||||
| 193 | 173.23 | 68.64 | 25.85 | ||||||
| 194 | 168.29 | 71.18 | 23.35 | ||||||
| 195 | 190.75 | 70.31 | 27.13 | ||||||
| 196 | 161.66 | 68.05 | 24.54 | ||||||
| 197 | 205.68 | 69.23 | 30.17 | ||||||
| 198 | 192.53 | 69.58 | 27.96 | ||||||
| 199 | 186.49 | 68.69 | 27.78 | ||||||
| 200 | 139.13 | 69.95 | 19.99 | ||||||
Project 9 instructions.docx
1. Employee PivotTables
You are the human resources director and you are interested in analyzing the hiring and compensation practices in your company. The Employee Data worksheet contains information about 1200 sales employees of your company. This information includes the Shift, Name, Location (city), Region, Position, and Salary for each employee. Complete the tasks by adding PivotTables to each worksheet in the workbook and then reference the worksheets to answer multiple choice questions.
1.1
Using the data on the Employee Data worksheet, insert a PivotTable on the "# Employees" worksheet to calculate the number of employees in each region. There are no blank salary cells and each employee accounts for one salary data point.
a. Place "Region" as the Row Labels of the PivotTable.
b. Use an appropriate presentation of Salary as the PivotTable Values.
1.2
Using the data on the Employee Data worksheet, insert a PivotTable on the "Experience" worksheet to calculate the average work experience by position for each location.
a. Construct the PivotTable with "Location" as the Row Labels.
b. Place "Position" as the Column Labels.
c. Use an appropriate presentation of "Experience" as the Values for the PivotTable.
1.3
Using the data on the Employee Data worksheet, insert a PivotTable on the "Max Salary" worksheet to calculate the maximum salary by shift and position.
a. Construct the PivotTable with Position as the Row Labels.
b. Place Shift as the Column Labels.
c. Use an appropriate presentation of Salary as the PivotTable values.
1.4
Using the data on the Employee Data worksheet, insert a PivotTable on the "Average Salary" worksheet to examine the average salary of employees based on their position and the shift they work.
a. Construct the PivotTable with "Position" as the Row Labels.
b. Place "Shift" as the Column Labels.
c. Use an appropriate presentation of "Salary" as the PivotTable Values.
1.5
Using the data on the Employee Data worksheet, insert a PivotTable on the "Position" worksheet to calculate the percentage of employees for each type of position by region. There are no blank salary cells and each employee accounts for one salary data point.
a. Construct the PivotTable with "Position" as the Row Labels.
b. Place "Region" as the Column Labels.
c. Use an appropriate presentation of Salary as the values field.
1.6
Using the data on the Employee Data worksheet, insert a PivotTable on the "Managers" worksheet to determine which locations have regional or sales managers.
a. Construct the PivotTable with "Location" as the Row Labels.
b. Place "Region" as the Column Labels.
c. Use the sum of "Experience" as the PivotTable Values.
d. Add "Position" as a "Report Filter".
e. Use the filter to display only "Regional Managers" and "Sales Managers" in the PivotTable.
1.7
Using the data on the Employee Data worksheet, insert a PivotTable on the "Salespersons" worksheet to calculate the percent of grand total each position makes up in each region.
a. Construct the PivotTable with "Position" as the Row Labels.
b. Place "Region" as the Column Labels.
c. Use an appropriate presentation of "Salary" as the PivotTable values.
ANSWER QUESTIONS 1-8
2
Consider the PivotTable on the # Employees worksheet. How many employees are in the West Region?
· 245
· 20 Million
· 13 Million
· 370
3
Consider the PivotTable on the Experience worksheet. What is the average number of years experience for Salespersons in Phoenix?
· 1495.5
· 1956.9
· 15.7
· 3.1
· 17
· 127.1
· 15.4
4
Consider the PivotTable on the Max Salary worksheet. What is the maximum salary for Regional Managers who work the morning shift?
· 202800
· 62100
· 179200
· 160000
· 100400
· 205600
5
Consider the PivotTable on the Average Salary worksheet. Is there evidence that employees working one shift make more than employees working the other shift?
· Yes
· No
6
Consider the PivotTable on the Position worksheet. Approximately what percentage of employees are Salespersons in the South region?
· 19%
· 94%
· 17%
· 95%
· 97%
· 93%
· 29%
7
Consider the PivotTable on the Managers worksheet. How many total years experience do managers of the East region have?
· 75
· 23
· 78
· 10
· 17
· 52
· 35
· 39
8
Consider the PivotTable on the Salespersons worksheet. What percentage of salaries are paid to the employees that hold a position of Salesperson?
· 30.70%
· 96.85%
· 100.00%
· 30.05%
· 17.61%
Project 9.xlsx
Employee Data
| Employee # | Shift | Name | Location | Region | Position | Experience | Salary | |
| 131 | Morning | Deanne Abbott | Philadelphia | East | Salesperson | 1 | $29,400 | |
| 793 | Afternoon | Santos Adams | New York | East | Salesperson | 7 | $25,400 | |
| 479 | Morning | Ronda Adkins | Phoenix | West | Salesperson | 10 | $82,200 | |
| 609 | Afternoon | Kory Adkins | Chicago | Midwest | Salesperson | 5 | $73,000 | |
| 652 | Morning | Charlene Adkins | Houston | South | Salesperson | 23 | $61,200 | |
| 675 | Afternoon | Tracy Aguilar | Chicago | Midwest | Salesperson | 16 | $27,200 | |
| 1057 | Afternoon | Marquis Aguilar | Houston | South | Intern | 0 | $14,300 | |
| 543 | Morning | Regina Aguirre | Los Angeles | West | Salesperson | 13 | $62,600 | |
| 636 | Morning | Francesca Alexander | New York | East | Salesperson | 14 | $48,200 | |
| 258 | Morning | Reba Allen | Chicago | Midwest | Salesperson | 5 | $43,500 | |
| 821 | Morning | Hattie Allison | San Antonio | South | Salesperson | 17 | $62,500 | |
| 907 | Afternoon | Luke Allison | Chicago | Midwest | Salesperson | 16 | $76,900 | |
| 1118 | Morning | Bernadine Allison | Chicago | Midwest | Salesperson | 9 | $69,900 | |
| 69 | Morning | Latoya Alvarado | Los Angeles | West | Salesperson | 9 | $84,700 | |
| 111 | Morning | Charmaine Alvarado | Dallas | South | Salesperson | 27 | $46,500 | |
| 306 | Morning | Angie Alvarado | Chicago | Midwest | Intern | 1 | $7,100 | |
| 801 | Afternoon | Rubin Alvarado | Los Angeles | West | Salesperson | 16 | $71,300 | |
| 1176 | Morning | Susie Alvarez | Houston | South | Salesperson | 30 | $60,300 | |
| 647 | Morning | Nettie Andersen | Dallas | South | Salesperson | 20 | $78,600 | |
| 759 | Morning | Deena Andersen | Detroit | Midwest | Salesperson | 10 | $38,000 | |
| 891 | Morning | Sofia Andersen | Houston | South | Salesperson | 10 | $74,900 | |
| 1107 | Morning | Cherie Andersen | Dallas | South | Salesperson | 3 | $50,000 | |
| 300 | Afternoon | August Andrade | San Antonio | South | Salesperson | 4 | $40,300 | |
| 656 | Morning | Johanna Andrade | Los Angeles | West | Salesperson | 13 | $44,300 | |
| 213 | Morning | Sophie Arellano | New York | East | Salesperson | 9 | $28,600 | |
| 1064 | Afternoon | Garrett Arias | Houston | South | Salesperson | 29 | $67,100 | |
| 852 | Afternoon | Vince Armstrong | Dallas | South | Salesperson | 5 | $80,600 | |
| 56 | Afternoon | Jeremy Arnold | Los Angeles | West | Salesperson | 12 | $82,100 | |
| 550 | Afternoon | Ethan Arnold | Detroit | Midwest | Salesperson | 26 | $34,400 | |
| 778 | Morning | Carissa Arnold | Dallas | South | Salesperson | 17 | $54,200 | |
| 1155 | Afternoon | Garrett Arroyo | Phoenix | West | Salesperson | 18 | $68,100 | |
| 251 | Morning | Rowena Ashley | Detroit | Midwest | Salesperson | 9 | $32,000 | |
| 1037 | Afternoon | Zachery Atkinson | Phoenix | West | Salesperson | 3 | $65,700 | |
| 90 | Afternoon | Shannon Avery | Philadelphia | East | Salesperson | 15 | $33,400 | |
| 921 | Morning | Josefa Avery | San Antonio | South | Salesperson | 24 | $82,300 | |
| 1154 | Morning | Yvette Avery | Phoenix | West | Salesperson | 5 | $31,700 | |
| 299 | Morning | Trudy Avila | Los Angeles | West | Salesperson | 25 | $79,500 | |
| 956 | Morning | Mai Avila | Philadelphia | East | Salesperson | 15 | $35,000 | |
| 469 | Afternoon | Victor Ayala | Phoenix | West | Salesperson | 19 | $81,700 | |
| 283 | Morning | Tami Ayers | Houston | South | Salesperson | 5 | $84,300 | |
| 844 | Afternoon | Charlie Ayers | Dallas | South | Salesperson | 14 | $40,000 | |
| 812 | Afternoon | Dewitt Baird | San Diego | West | Salesperson | 17 | $34,100 | |
| 500 | Morning | Ophelia Baker | Houston | South | Salesperson | 13 | $52,200 | |
| 704 | Morning | Joan Baldwin | Detroit | Midwest | Salesperson | 7 | $43,100 | |
| 225 | Morning | Dianne Ball | Philadelphia | East | Salesperson | 30 | $54,500 | |
| 707 | Morning | Hilary Ball | Phoenix | West | Salesperson | 11 | $55,800 | |
| 1077 | Morning | Brigitte Ball | Detroit | Midwest | Salesperson | 27 | $68,900 | |
| 1184 | Afternoon | Thaddeus Ball | Los Angeles | West | Salesperson | 30 | $27,700 | |
| 885 | Afternoon | Mac Barnes | Los Angeles | West | Salesperson | 16 | $82,900 | |
| 112 | Afternoon | Ollie Barrera | Chicago | Midwest | Salesperson | 17 | $76,200 | |
| 356 | Afternoon | Harold Barrera | Philadelphia | East | Salesperson | 6 | $50,200 | |
| 408 | Afternoon | Reynaldo Barrett | New York | East | Salesperson | 17 | $48,200 | |
| 442 | Afternoon | Jason Barron | Philadelphia | East | Salesperson | 19 | $83,900 | |
| 1082 | Morning | Robert Barron | San Antonio | South | Salesperson | 2 | $63,700 | |
| 266 | Afternoon | Jamar Barry | San Diego | West | Salesperson | 19 | $69,900 | |
| 692 | Afternoon | Mason Bartlett | Chicago | Midwest | Salesperson | 16 | $50,800 | |
| 1127 | Afternoon | Willard Bartlett | Philadelphia | East | Salesperson | 27 | $54,300 | |
| 670 | Afternoon | Erin Barton | Phoenix | West | Salesperson | 4 | $46,000 | |
| 1042 | Morning | Glenda Barton | New York | East | Salesperson | 15 | $46,900 | |
| 685 | Afternoon | John Bass | Detroit | Midwest | Salesperson | 15 | $66,700 | |
| 39 | Afternoon | Clarence Bates | Houston | South | Salesperson | 29 | $56,000 | |
| 565 | Afternoon | Mac Bates | Los Angeles | West | Intern | 0 | $7,300 | |
| 811 | Afternoon | Angel Bates | New York | East | Salesperson | 5 | $81,100 | |
| 949 | Morning | Terra Bates | Dallas | South | Salesperson | 30 | $60,200 | |
| 1003 | Morning | Patty Bates | Los Angeles | West | Salesperson | 15 | $70,500 | |
| 32 | Morning | Kimberly Baxter | Detroit | Midwest | Intern | 1 | $5,600 | |
| 174 | Afternoon | Milton Bean | Houston | South | Salesperson | 24 | $25,900 | |
| 474 | Morning | Etta Beard | Los Angeles | West | Salesperson | 21 | $74,900 | |
| 481 | Morning | Mitzi Beard | Detroit | Midwest | Salesperson | 5 | $29,100 | |
| 375 | Afternoon | Luis Beck | Phoenix | West | Salesperson | 21 | $74,000 | |
| 696 | Afternoon | Brandon Beck | Philadelphia | East | Salesperson | 24 | $62,100 | |
| 986 | Afternoon | Edwardo Beck | Phoenix | West | Salesperson | 29 | $36,300 | |
| 908 | Morning | Norma Becker | Detroit | Midwest | Salesperson | 25 | $65,700 | |
| 1099 | Morning | Allyson Becker | Chicago | Midwest | Salesperson | 20 | $49,900 | |
| 110 | Afternoon | Roberto Bell | San Antonio | South | Salesperson | 20 | $65,700 | |
| 818 | Afternoon | Mohamed Bell | Dallas | South | Salesperson | 8 | $44,800 | |
| 1070 | Afternoon | Moises Bender | Houston | South | Salesperson | 25 | $78,500 | |
| 1021 | Afternoon | Aurelio Benitez | Los Angeles | West | Salesperson | 5 | $75,500 | |
| 538 | Morning | Edwina Benjamin | Philadelphia | East | Salesperson | 25 | $82,700 | |
| 405 | Afternoon | Alden Bentley | San Diego | West | Intern | 0 | $7,700 | |
| 533 | Afternoon | Collin Bentley | Philadelphia | East | Salesperson | 14 | $74,300 | |
| 1179 | Morning | Katharine Bentley | New York | East | Salesperson | 4 | $50,600 | |
| 203 | Afternoon | Solomon Benton | Detroit | Midwest | Salesperson | 27 | $58,700 | |
| 1147 | Morning | Georgia Benton | Phoenix | West | Salesperson | 4 | $83,600 | |
| 347 | Afternoon | Pierre Bernard | Detroit | Midwest | Salesperson | 2 | $69,900 | |
| 1153 | Afternoon | Quincy Bernard | Houston | South | Salesperson | 17 | $84,200 | |
| 718 | Morning | Doris Berry | San Diego | West | Salesperson | 28 | $56,000 | |
| 804 | Morning | Jennifer Berry | San Antonio | South | Salesperson | 3 | $38,200 | |
| 897 | Afternoon | Boris Best | Dallas | South | Salesperson | 9 | $61,100 | |
| 395 | Morning | Lynette Bird | San Antonio | South | Sales Manager | 17 | $65,800 | |
| 146 | Afternoon | Erin Black | Dallas | South | Salesperson | 15 | $48,000 | |
| 1007 | Afternoon | Horacio Black | New York | East | Salesperson | 9 | $25,800 | |
| 514 | Morning | Molly Blackburn | New York | East | Salesperson | 19 | $81,000 | |
| 976 | Afternoon | Elbert Blackwell | San Diego | West | Salesperson | 9 | $54,600 | |
| 991 | Afternoon | Weldon Blair | New York | East | Salesperson | 1 | $45,900 | |
| 1040 | Morning | Jolene Blair | Dallas | South | Salesperson | 24 | $31,200 | |
| 1074 | Afternoon | Rocky Blair | Dallas | South | Salesperson | 22 | $54,600 | |
| 150 | Afternoon | Marc Blanchard | Detroit | Midwest | Salesperson | 21 | $54,000 | |
| 322 | Afternoon | Wilbur Blanchard | New York | East | Salesperson | 26 | $81,100 | |
| 649 | Afternoon | Blake Blanchard | New York | East | Salesperson | 28 | $27,400 | |
| 247 | Afternoon | Nicholas Blankenship | Dallas | South | Salesperson | 15 | $60,900 | |
| 689 | Morning | Pauline Blevins | Los Angeles | West | Salesperson | 5 | $45,100 | |
| 990 | Afternoon | Kenneth Bolton | San Antonio | South | Salesperson | 11 | $46,900 | |
| 177 | Morning | Dianna Booker | Houston | South | Salesperson | 9 | $29,100 | |
| 888 | Afternoon | Johnny Booker | New York | East | Salesperson | 6 | $57,500 | |
| 1164 | Morning | Lindsey Booker | San Antonio | South | Salesperson | 1 | $29,500 | |
| 635 | Morning | Fern Boone | Detroit | Midwest | Salesperson | 26 | $57,100 | |
| 984 | Afternoon | Bud Boone | Dallas | South | Salesperson | 6 | $62,400 | |
| 783 | Morning | Sheri Bowen | Los Angeles | West | Salesperson | 3 | $84,800 | |
| 580 | Morning | Alexandria Bowers | Philadelphia | East | Salesperson | 6 | $31,300 | |
| 590 | Morning | Lucy Bowers | San Antonio | South | Salesperson | 18 | $33,400 | |
| 612 | Morning | Kelly Bowers | Los Angeles | West | Salesperson | 5 | $65,400 | |
| 981 | Afternoon | Sonny Bowers | Phoenix | West | Salesperson | 24 | $45,700 | |
| 246 | Morning | Amie Boyd | Phoenix | West | Salesperson | 14 | $28,300 | |
| 657 | Afternoon | Jean Boyer | Houston | South | Salesperson | 30 | $49,800 | |
| 1083 | Morning | Cherie Boyle | New York | East | Salesperson | 28 | $49,700 | |
| 619 | Morning | Silvia Bradford | Los Angeles | West | Salesperson | 1 | $76,200 | |
| 83 | Afternoon | Jefferey Brady | Dallas | South | Salesperson | 6 | $70,500 | |
| 483 | Morning | Cathryn Brady | Los Angeles | West | Salesperson | 1 | $55,000 | |
| 734 | Afternoon | Cyrus Brady | Detroit | Midwest | Salesperson | 10 | $82,700 | |
| 413 | Afternoon | Jarred Branch | Phoenix | West | Salesperson | 3 | $78,300 | |
| 924 | Afternoon | Dudley Branch | Detroit | Midwest | Salesperson | 2 | $55,300 | |
| 51 | Morning | Hattie Brandt | Los Angeles | West | Salesperson | 22 | $72,400 | |
| 1058 | Afternoon | Wallace Brandt | Houston | South | Salesperson | 25 | $65,000 | |
| 100 | Afternoon | Tomas Brewer | Los Angeles | West | Salesperson | 6 | $46,500 | |
| 1051 | Morning | Sara Brewer | Dallas | South | Salesperson | 3 | $84,200 | |
| 15 | Morning | Concetta Bridges | Phoenix | West | Salesperson | 1 | $36,200 | |
| 325 | Afternoon | Donovan Bridges | New York | East | Salesperson | 26 | $26,200 | |
| 252 | Morning | Marta Briggs | Philadelphia | East | Salesperson | 13 | $58,700 | |
| 492 | Afternoon | Logan Briggs | Phoenix | West | Salesperson | 2 | $68,600 | |
| 596 | Morning | Blanche Bright | Houston | South | Salesperson | 27 | $77,100 | |
| 1152 | Afternoon | Grady Bright | Detroit | Midwest | Salesperson | 7 | $70,100 | |
| 951 | Morning | Darcy Brock | San Diego | West | Salesperson | 7 | $59,600 | |
| 301 | Afternoon | Hugh Brooks | Dallas | South | Salesperson | 28 | $41,200 | |
| 701 | Morning | Latonya Brooks | Philadelphia | East | Salesperson | 4 | $61,800 | |
| 808 | Morning | Sara Brooks | Phoenix | West | Salesperson | 12 | $77,300 | |
| 1094 | Afternoon | Sydney Browning | Detroit | Midwest | Salesperson | 3 | $53,000 | |
| 923 | Afternoon | Marquis Bruce | San Antonio | South | Salesperson | 12 | $56,400 | |
| 1045 | Morning | Opal Bruce | Detroit | Midwest | Salesperson | 3 | $72,200 | |
| 288 | Afternoon | Otto Bryant | San Antonio | South | Salesperson | 23 | $73,000 | |
| 197 | Afternoon | Brent Buchanan | San Diego | West | Salesperson | 15 | $31,600 | |
| 1182 | Afternoon | Alonzo Buck | New York | East | Salesperson | 13 | $69,900 | |
| 523 | Afternoon | Dane Bullock | Los Angeles | West | Salesperson | 22 | $81,900 | |
| 123 | Afternoon | Eugene Burgess | Philadelphia | East | Salesperson | 20 | $60,700 | |
| 200 | Afternoon | Karl Burgess | Chicago | Midwest | Salesperson | 9 | $53,800 | |
| 133 | Afternoon | Tomas Burke | Dallas | South | Salesperson | 8 | $60,200 | |
| 267 | Morning | Eula Burnett | Phoenix | West | Salesperson | 3 | $68,400 | |
| 309 | Morning | Monique Burnett | Houston | South | Salesperson | 28 | $30,200 | |
| 1196 | Morning | Cherry Burnett | Detroit | Midwest | Salesperson | 26 | $82,400 | |
| 862 | Afternoon | Mel Bush | New York | East | Salesperson | 30 | $41,800 | |
| 926 | Afternoon | Jayson Byrd | Chicago | Midwest | Regional Manager | 22 | $198,800 | |
| 114 | Afternoon | Lyman Cabrera | Detroit | Midwest | Salesperson | 28 | $37,300 | |
| 816 | Morning | Jimmie Cabrera | San Diego | West | Salesperson | 5 | $81,800 | |
| 996 | Morning | Isabelle Cabrera | Philadelphia | East | Salesperson | 25 | $39,600 | |
| 64 | Afternoon | Danial Cain | New York | East | Salesperson | 16 | $39,800 | |
| 97 | Afternoon | Adan Cain | Philadelphia | East | Salesperson | 28 | $78,500 | |
| 524 | Afternoon | Mitch Cain | New York | East | Salesperson | 8 | $42,100 | |
| 719 | Afternoon | Omar Cain | Dallas | South | Salesperson | 1 | $65,200 | |
| 381 | Morning | Deana Calderon | Philadelphia | East | Salesperson | 26 | $76,900 | |
| 571 | Morning | May Calderon | Chicago | Midwest | Salesperson | 27 | $53,600 | |
| 756 | Morning | Krystal Caldwell | Houston | South | Salesperson | 9 | $42,400 | |
| 341 | Morning | Reyna Calhoun | Philadelphia | East | Salesperson | 20 | $51,800 | |
| 1161 | Afternoon | Eugenio Calhoun | Philadelphia | East | Salesperson | 14 | $78,400 | |
| 1145 | Afternoon | Trenton Callahan | San Diego | West | Salesperson | 12 | $27,700 | |
| 593 | Afternoon | Collin Campbell | Dallas | South | Salesperson | 5 | $73,800 | |
| 154 | Morning | Patrice Campos | San Antonio | South | Salesperson | 30 | $48,600 | |
| 447 | Afternoon | Tyson Cannon | Philadelphia | East | Intern | 1 | $9,400 | |
| 592 | Afternoon | Tristan Cantrell | Dallas | South | Salesperson | 30 | $77,100 | |
| 239 | Morning | Carole Cantu | Detroit | Midwest | Salesperson | 2 | $73,700 | |
| 1091 | Morning | Stephanie Cantu | New York | East | Salesperson | 2 | $54,200 | |
| 284 | Afternoon | Brendan Cardenas | Los Angeles | West | Salesperson | 2 | $73,500 | |
| 391 | Afternoon | Rodrick Carey | Philadelphia | East | Salesperson | 13 | $69,900 | |
| 1137 | Morning | Caroline Carey | Philadelphia | East | Salesperson | 11 | $38,900 | |
| 878 | Morning | Bianca Carlson | Houston | South | Salesperson | 7 | $45,100 | |
| 1110 | Afternoon | Spencer Carney | Phoenix | West | Salesperson | 21 | $67,200 | |
| 1158 | Afternoon | Andrew Carney | San Diego | West | Salesperson | 27 | $71,600 | |
| 367 | Afternoon | Jasper Carpenter | Phoenix | West | Salesperson | 26 | $26,300 | |
| 807 | Afternoon | Erin Carpenter | Los Angeles | West | Salesperson | 7 | $39,700 | |
| 27 | Afternoon | Dannie Carr | Detroit | Midwest | Salesperson | 1 | $75,800 | |
| 320 | Afternoon | Paul Carr | Phoenix | West | Salesperson | 23 | $35,100 | |
| 172 | Afternoon | Shelby Carrillo | Los Angeles | West | Salesperson | 4 | $53,900 | |
| 85 | Afternoon | Sung Carroll | Dallas | South | Salesperson | 29 | $64,400 | |
| 838 | Afternoon | Damion Carroll | San Antonio | South | Intern | 0 | $7,600 | |
| 835 | Morning | Marsha Carson | Los Angeles | West | Salesperson | 28 | $46,900 | |
| 857 | Morning | Mary Carson | San Diego | West | Salesperson | 29 | $65,300 | |
| 810 | Afternoon | Alden Castaneda | Detroit | Midwest | Salesperson | 7 | $70,400 | |
| 967 | Morning | Roslyn Castaneda | Chicago | Midwest | Salesperson | 24 | $42,700 | |
| 196 | Afternoon | Justin Castillo | Chicago | Midwest | Intern | 1 | $7,900 | |
| 504 | Afternoon | Milo Chambers | Los Angeles | West | Salesperson | 29 | $73,600 | |
| 792 | Morning | Sally Chambers | New York | East | Salesperson | 13 | $76,200 | |
| 1195 | Afternoon | Gail Chambers | Chicago | Midwest | Salesperson | 12 | $69,500 | |
| 226 | Morning | Marylou Chandler | Houston | South | Salesperson | 26 | $33,600 | |
| 586 | Afternoon | Devin Chaney | New York | East | Salesperson | 20 | $30,600 | |
| 230 | Afternoon | Wade Chang | Dallas | South | Salesperson | 1 | $79,800 | |
| 637 | Morning | Louella Chapman | Los Angeles | West | Salesperson | 21 | $69,200 | |
| 449 | Afternoon | Seth Charles | Houston | South | Salesperson | 23 | $36,500 | |
| 401 | Morning | Glenna Chase | Phoenix | West | Salesperson | 22 | $81,300 | |
| 505 | Afternoon | Nelson Chase | Detroit | Midwest | Salesperson | 26 | $55,400 | |
| 457 | Morning | Patsy Cherry | Dallas | South | Salesperson | 17 | $56,900 | |
| 623 | Afternoon | Darin Choi | Los Angeles | West | Salesperson | 17 | $57,000 | |
| 297 | Afternoon | Alfredo Christensen | Chicago | Midwest | Salesperson | 3 | $66,500 | |
| 595 | Morning | Aida Christian | Chicago | Midwest | Salesperson | 8 | $48,200 | |
| 310 | Morning | Dolly Church | New York | East | Salesperson | 5 | $47,000 | |
| 364 | Morning | Marla Church | Chicago | Midwest | Salesperson | 11 | $41,900 | |
| 1117 | Afternoon | Sheldon Church | Detroit | Midwest | Salesperson | 13 | $40,700 | |
| 445 | Morning | Hannah Clark | San Diego | West | Intern | 1 | $7,800 | |
| 709 | Morning | Loraine Clark | Detroit | Midwest | Salesperson | 16 | $71,700 | |
| 316 | Morning | Dominique Clarke | Detroit | Midwest | Salesperson | 5 | $74,200 | |
| 726 | Afternoon | Emilio Clarke | Detroit | Midwest | Salesperson | 24 | $75,000 | |
| 1002 | Morning | Rena Clarke | Los Angeles | West | Salesperson | 3 | $35,500 | |
| 903 | Afternoon | Domingo Clements | Dallas | South | Salesperson | 22 | $68,800 | |
| 961 | Morning | Violet Clements | Chicago | Midwest | Salesperson | 4 | $75,100 | |
| 979 | Morning | Lilian Clements | Dallas | South | Salesperson | 4 | $67,400 | |
| 75 | Afternoon | Jerome Cline | New York | East | Salesperson | 10 | $35,200 | |
| 817 | Morning | Gladys Cobb | New York | East | Salesperson | 22 | $37,500 | |
| 724 | Afternoon | Jesus Cochran | Los Angeles | West | Salesperson | 3 | $43,300 | |
| 1025 | Morning | Maritza Cole | San Diego | West | Salesperson | 30 | $55,100 | |
| 305 | Morning | Edna Coleman | Phoenix | West | Salesperson | 19 | $46,800 | |
| 667 | Morning | Paige Coleman | San Antonio | South | Salesperson | 13 | $65,600 | |
| 947 | Morning | Karen Coleman | Phoenix | West | Salesperson | 11 | $72,100 | |
| 605 | Morning | Marianne Collier | Houston | South | Salesperson | 28 | $76,100 | |
| 615 | Afternoon | Anderson Collier | Chicago | Midwest | Salesperson | 11 | $74,300 | |
| 938 | Afternoon | Carlton Collier | Detroit | Midwest | Salesperson | 23 | $74,900 | |
| 1122 | Morning | Noreen Colon | Houston | South | Salesperson | 24 | $40,400 | |
| 1175 | Afternoon | Romeo Colon | Phoenix | West | Salesperson | 21 | $71,500 | |
| 851 | Afternoon | Dave Combs | Detroit | Midwest | Salesperson | 22 | $59,400 | |
| 313 | Afternoon | Faustino Compton | Phoenix | West | Salesperson | 2 | $25,300 | |
| 850 | Afternoon | Clayton Compton | Houston | South | Salesperson | 1 | $69,300 | |
| 129 | Afternoon | Gerardo Conley | Phoenix | West | Salesperson | 24 | $82,100 | |
| 980 | Morning | Liz Conley | Detroit | Midwest | Salesperson | 9 | $78,700 | |
| 1067 | Morning | Jerry Conley | Chicago | Midwest | Salesperson | 22 | $49,500 | |
| 1151 | Afternoon | Mack Conley | Chicago | Midwest | Salesperson | 4 | $50,300 | |
| 351 | Morning | Megan Conrad | New York | East | Salesperson | 23 | $83,700 | |
| 430 | Afternoon | Marcel Conrad | Houston | South | Salesperson | 10 | $80,900 | |
| 584 | Afternoon | Cody Conrad | Los Angeles | West | Salesperson | 6 | $58,300 | |
| 1081 | Afternoon | Major Conway | Houston | South | Salesperson | 13 | $27,000 | |
| 1160 | Morning | Leslie Conway | Philadelphia | East | Salesperson | 9 | $73,100 | |
| 415 | Morning | Robin Cook | Chicago | Midwest | Salesperson | 6 | $53,500 | |
| 95 | Afternoon | Maxwell Cooke | Dallas | South | Salesperson | 30 | $41,600 | |
| 913 | Morning | Esther Cooke | Houston | South | Salesperson | 13 | $68,100 | |
| 927 | Afternoon | Willard Cooley | Dallas | South | Salesperson | 27 | $30,200 | |
| 14 | Afternoon | Leonard Cooper | New York | East | Salesperson | 25 | $27,300 | |
| 407 | Afternoon | Marcelino Cooper | San Diego | West | Salesperson | 14 | $65,800 | |
| 875 | Afternoon | Maynard Copeland | Chicago | Midwest | Salesperson | 7 | $62,200 | |
| 964 | Morning | Leigh Cordova | San Antonio | South | Salesperson | 29 | $68,900 | |
| 673 | Afternoon | Gary Cortez | San Antonio | South | Salesperson | 20 | $73,500 | |
| 243 | Morning | Stacey Cowan | Dallas | South | Salesperson | 12 | $61,500 | |
| 983 | Morning | Cathryn Cowan | Phoenix | West | Salesperson | 19 | $57,700 | |
| 900 | Afternoon | Emilio Craig | Dallas | South | Salesperson | 6 | $76,000 | |
| 321 | Morning | Leonor Crane | New York | East | Salesperson | 14 | $42,400 | |
| 241 | Afternoon | Jeffry Crawford | New York | East | Salesperson | 10 | $74,900 | |
| 998 | Afternoon | Chester Crawford | San Diego | West | Salesperson | 14 | $76,900 | |
| 732 | Morning | Nadia Cruz | Dallas | South | Intern | 0 | $12,600 | |
| 650 | Morning | Nona Cummings | San Antonio | South | Salesperson | 20 | $67,800 | |
| 23 | Morning | Lillie Cunningham | Philadelphia | East | Salesperson | 12 | $80,300 | |
| 894 | Afternoon | Refugio Curry | San Diego | West | Salesperson | 2 | $79,500 | |
| 941 | Morning | Elisabeth Curtis | New York | East | Salesperson | 29 | $58,700 | |
| 579 | Afternoon | William Dalton | New York | East | Salesperson | 12 | $42,700 | |
| 582 | Morning | Marcella Dalton | Detroit | Midwest | Salesperson | 6 | $70,100 | |
| 754 | Afternoon | Neil Dalton | San Diego | West | Salesperson | 3 | $57,300 | |
| 1163 | Afternoon | Erick Dalton | Chicago | Midwest | Salesperson | 23 | $32,600 | |
| 62 | Afternoon | Roger Daniels | San Diego | West | Salesperson | 25 | $71,000 | |
| 729 | Morning | Willa Daugherty | Dallas | South | Intern | 1 | $7,600 | |
| 46 | Afternoon | Houston David | Houston | South | Salesperson | 18 | $48,600 | |
| 71 | Afternoon | Devon Davies | Phoenix | West | Salesperson | 4 | $64,000 | |
| 179 | Afternoon | Alvaro Davila | Los Angeles | West | Regional Manager | 18 | $205,600 | |
| 219 | Afternoon | Delmar Davila | Phoenix | West | Salesperson | 9 | $25,900 | |
| 883 | Afternoon | Bernardo Davila | San Antonio | South | Salesperson | 3 | $75,300 | |
| 12 | Morning | Hallie Davis | Philadelphia | East | Salesperson | 4 | $80,000 | |
| 287 | Afternoon | Vaughn Davis | Dallas | South | Sales Manager | 14 | $96,700 | |
| 695 | Morning | Pearlie Davis | Detroit | Midwest | Intern | 1 | $10,000 | |
| 124 | Afternoon | Junior Dawson | San Diego | West | Salesperson | 15 | $72,700 | |
| 369 | Afternoon | Landon Dean | New York | East | Salesperson | 8 | $48,900 | |
| 777 | Morning | Johnnie Dean | Detroit | Midwest | Salesperson | 29 | $59,100 | |
| 1150 | Morning | Vickie Dean | San Diego | West | Salesperson | 23 | $46,200 | |
| 170 | Afternoon | Vern Decker | San Antonio | South | Salesperson | 30 | $42,600 | |
| 295 | Afternoon | Grover Decker | New York | East | Salesperson | 20 | $84,900 | |
| 155 | Afternoon | Loyd Deleon | Phoenix | West | Sales Manager | 17 | $78,900 | |
| 645 | Morning | Autumn Deleon | Philadelphia | East | Intern | 0 | $7,400 | |
| 686 | Morning | Robyn Deleon | Detroit | Midwest | Salesperson | 18 | $36,800 | |
| 914 | Morning | Ilene Deleon | New York | East | Salesperson | 18 | $62,100 | |
| 440 | Morning | Graciela Dennis | New York | East | Salesperson | 8 | $72,900 | |
| 654 | Afternoon | Manuel Dennis | Chicago | Midwest | Salesperson | 22 | $47,900 | |
| 819 | Morning | Kristine Dennis | Dallas | South | Salesperson | 9 | $62,400 | |
| 37 | Afternoon | Shaun Diaz | San Diego | West | Salesperson | 28 | $81,500 | |
| 61 | Afternoon | Mack Diaz | Phoenix | West | Salesperson | 5 | $59,800 | |
| 498 | Afternoon | Gustavo Diaz | Phoenix | West | Salesperson | 2 | $38,000 | |
| 535 | Morning | Imelda Dillon | Dallas | South | Salesperson | 9 | $60,100 | |
| 854 | Morning | Reva Dillon | Chicago | Midwest | Salesperson | 12 | $46,800 | |
| 1078 | Morning | Elizabeth Dixon | Phoenix | West | Salesperson | 28 | $72,000 | |
| 144 | Afternoon | Jamaal Dodson | San Diego | West | Salesperson | 5 | $28,400 | |
| 216 | Morning | Blanca Dodson | Chicago | Midwest | Salesperson | 19 | $36,000 | |
| 1065 | Afternoon | Carmelo Dodson | New York | East | Salesperson | 15 | $68,700 | |
| 304 | Afternoon | Rolland Dominguez | Phoenix | West | Salesperson | 4 | $80,900 | |
| 1121 | Afternoon | Nolan Dominguez | Philadelphia | East | Salesperson | 17 | $27,400 | |
| 116 | Afternoon | Daniel Donovan | Los Angeles | West | Salesperson | 4 | $66,800 | |
| 2 | Afternoon | Reynaldo Douglas | Philadelphia | East | Salesperson | 23 | $81,200 | |
| 614 | Afternoon | Randell Douglas | New York | East | Salesperson | 3 | $29,000 | |
| 960 | Afternoon | Tracy Douglas | San Diego | West | Salesperson | 10 | $41,200 | |
| 373 | Morning | Magdalena Downs | Dallas | South | Salesperson | 12 | $37,200 | |
| 594 | Afternoon | Mario Downs | San Diego | West | Salesperson | 14 | $27,400 | |
| 359 | Afternoon | Jimmy Doyle | Houston | South | Salesperson | 30 | $35,800 | |
| 564 | Afternoon | Edgar Doyle | Dallas | South | Salesperson | 28 | $80,600 | |
| 648 | Morning | Krystal Doyle | Los Angeles | West | Salesperson | 10 | $51,000 | |
| 757 | Morning | Geneva Doyle | Dallas | South | Intern | 1 | $12,100 | |
| 231 | Morning | Linda Drake | San Diego | West | Salesperson | 10 | $75,700 | |
| 666 | Afternoon | Sam Duarte | Philadelphia | East | Salesperson | 8 | $71,800 | |
| 106 | Morning | Bernice Duffy | Chicago | Midwest | Salesperson | 14 | $29,000 | |
| 282 | Morning | Melissa Duffy | Detroit | Midwest | Sales Manager | 10 | $90,300 | |
| 575 | Morning | Natalie Duffy | New York | East | Salesperson | 29 | $34,900 | |
| 1194 | Afternoon | Tommy Duffy | Dallas | South | Salesperson | 15 | $56,100 | |
| 752 | Morning | Loretta Duncan | New York | East | Salesperson | 24 | $67,300 | |
| 416 | Afternoon | Scottie Dunlap | Los Angeles | West | Salesperson | 15 | $34,000 | |
| 797 | Afternoon | Quincy Dunlap | Dallas | South | Salesperson | 20 | $33,500 | |
| 833 | Afternoon | Evan Dunlap | Phoenix | West | Salesperson | 23 | $37,100 | |
| 1066 | Morning | Lottie Dunlap | Houston | South | Salesperson | 30 | $47,000 | |
| 26 | Morning | Carmen Dunn | Los Angeles | West | Salesperson | 15 | $66,100 | |
| 877 | Afternoon | Riley Dunn | San Antonio | South | Salesperson | 21 | $51,800 | |
| 382 | Afternoon | Randall Durham | Dallas | South | Salesperson | 12 | $35,100 | |
| 569 | Morning | Trisha Durham | San Antonio | South | Salesperson | 19 | $34,000 | |
| 638 | Morning | Marcia Durham | Los Angeles | West | Salesperson | 22 | $76,000 | |
| 1069 | Afternoon | Duncan Durham | San Antonio | South | Salesperson | 11 | $48,700 | |
| 588 | Morning | Leigh Eaton | Los Angeles | West | Salesperson | 25 | $49,000 | |
| 600 | Afternoon | Colin Eaton | Detroit | Midwest | Salesperson | 7 | $63,900 | |
| 795 | Morning | Latonya Edwards | Detroit | Midwest | Salesperson | 12 | $40,700 | |
| 750 | Afternoon | Denis Elliott | San Diego | West | Salesperson | 30 | $31,800 | |
| 837 | Afternoon | Harley Elliott | Detroit | Midwest | Salesperson | 29 | $74,300 | |
| 344 | Morning | Jenna Ellis | San Diego | West | Salesperson | 17 | $80,300 | |
| 1142 | Morning | Margo Escobar | San Diego | West | Salesperson | 26 | $61,200 | |
| 400 | Afternoon | Javier Estes | San Diego | West | Salesperson | 20 | $29,600 | |
| 517 | Afternoon | Lupe Estrada | Dallas | South | Salesperson | 30 | $70,800 | |
| 1183 | Morning | Tamera Estrada | New York | East | Intern | 1 | $7,200 | |
| 529 | Morning | Valerie Evans | New York | East | Salesperson | 5 | $40,300 | |
| 503 | Morning | Vicki Everett | San Diego | West | Salesperson | 14 | $45,400 | |
| 441 | Morning | Ina Ewing | Philadelphia | East | Salesperson | 9 | $75,100 | |
| 88 | Morning | Josie Farley | Philadelphia | East | Salesperson | 23 | $34,500 | |
| 484 | Morning | Maryellen Farmer | Phoenix | West | Salesperson | 2 | $57,300 | |
| 975 | Morning | Vera Farmer | San Diego | West | Salesperson | 13 | $31,700 | |
| 527 | Morning | Bethany Farrell | Houston | South | Salesperson | 3 | $82,700 | |
| 744 | Afternoon | Roosevelt Farrell | Chicago | Midwest | Intern | 0 | $13,000 | |
| 70 | Afternoon | Daryl Faulkner | Los Angeles | West | Sales Manager | 17 | $70,300 | |
| 148 | Afternoon | Tanner Faulkner | New York | East | Salesperson | 4 | $67,400 | |
| 399 | Afternoon | Ben Fields | Dallas | South | Salesperson | 29 | $25,000 | |
| 651 | Afternoon | Rodolfo Fields | San Diego | West | Salesperson | 22 | $81,500 | |
| 922 | Afternoon | Jed Fitzgerald | Los Angeles | West | Salesperson | 8 | $70,600 | |
| 403 | Morning | Goldie Fletcher | San Antonio | South | Salesperson | 11 | $71,800 | |
| 467 | Afternoon | Wilford Fletcher | Houston | South | Salesperson | 21 | $30,900 | |
| 583 | Morning | Yvette Fletcher | Chicago | Midwest | Salesperson | 14 | $67,300 | |
| 1036 | Afternoon | Shirley Flowers | Philadelphia | East | Salesperson | 12 | $84,500 | |
| 856 | Afternoon | Mack Floyd | Phoenix | West | Salesperson | 2 | $76,100 | |
| 293 | Afternoon | Vince Flynn | Houston | South | Salesperson | 5 | $59,400 | |
| 1104 | Afternoon | Son Foley | San Diego | West | Intern | 0 | $5,500 | |
| 206 | Afternoon | Cody Forbes | Philadelphia | East | Salesperson | 25 | $28,100 | |
| 368 | Morning | Rhea Forbes | San Antonio | South | Salesperson | 27 | $28,100 | |
| 1113 | Afternoon | Denver Forbes | Los Angeles | West | Salesperson | 1 | $34,800 | |
| 160 | Morning | Lilian Ford | San Diego | West | Salesperson | 20 | $83,000 | |
| 99 | Morning | Maria Foster | San Diego | West | Salesperson | 29 | $31,300 | |
| 208 | Afternoon | Anderson Foster | Dallas | South | Salesperson | 9 | $53,300 | |
| 58 | Morning | Lakeisha Francis | Philadelphia | East | Salesperson | 6 | $41,900 | |
| 84 | Afternoon | Augustine Francis | Philadelphia | East | Intern | 1 | $12,700 | |
| 358 | Afternoon | Miles Francis | San Antonio | South | Salesperson | 21 | $60,000 | |
| 1017 | Morning | May Franco | Detroit | Midwest | Salesperson | 20 | $33,400 | |
| 66 | Afternoon | Olen Frank | New York | East | Intern | 1 | $12,700 | |
| 426 | Afternoon | Odis Freeman | Philadelphia | East | Salesperson | 27 | $72,600 | |
| 825 | Afternoon | Kennith Freeman | Houston | South | Salesperson | 21 | $61,300 | |
| 465 | Afternoon | Raul French | Chicago | Midwest | Salesperson | 27 | $83,200 | |
| 240 | Morning | Tanisha Friedman | Dallas | South | Salesperson | 6 | $34,600 | |
| 831 | Morning | Trisha Friedman | Philadelphia | East | Salesperson | 7 | $69,300 | |
| 142 | Morning | Tamera Fritz | Chicago | Midwest | Sales Manager | 30 | $100,400 | |
| 568 | Morning | Jewell Fritz | San Diego | West | Salesperson | 23 | $37,400 | |
| 217 | Afternoon | Jordan Frost | Philadelphia | East | Salesperson | 24 | $79,800 | |
| 265 | Afternoon | Ronnie Frost | New York | East | Salesperson | 20 | $47,200 | |
| 285 | Morning | Alyce Frost | San Diego | West | Intern | 0 | $10,100 | |
| 428 | Afternoon | Gail Fry | San Diego | West | Salesperson | 18 | $25,600 | |
| 101 | Morning | Janelle Frye | San Antonio | South | Salesperson | 29 | $84,000 | |
| 289 | Morning | Rita Fuller | Dallas | South | Salesperson | 8 | $70,900 | |
| 384 | Morning | Raquel Fuller | Detroit | Midwest | Salesperson | 8 | $72,800 | |
| 691 | Morning | Lourdes Gaines | Dallas | South | Salesperson | 17 | $84,900 | |
| 335 | Afternoon | Joseph Gallagher | Detroit | Midwest | Salesperson | 24 | $33,100 | |
| 427 | Afternoon | Jimmie Gallagher | San Diego | West | Salesperson | 7 | $32,900 | |
| 1006 | Afternoon | Darrel Gallagher | Detroit | Midwest | Salesperson | 21 | $78,200 | |
| 1114 | Morning | Susie Gallagher | San Antonio | South | Salesperson | 24 | $46,400 | |
| 204 | Morning | Nita Galloway | Philadelphia | East | Salesperson | 18 | $44,600 | |
| 545 | Morning | Roslyn Galloway | Los Angeles | West | Salesperson | 28 | $83,500 | |
| 634 | Afternoon | Casey Gamble | Dallas | South | Salesperson | 3 | $77,000 | |
| 658 | Afternoon | Malcolm Gamble | Dallas | South | Salesperson | 20 | $57,700 | |
| 1005 | Afternoon | Hollis Gamble | Houston | South | Salesperson | 21 | $62,600 | |
| 50 | Morning | Lana Garcia | Los Angeles | West | Salesperson | 12 | $67,000 | |
| 182 | Morning | Samantha Garcia | San Diego | West | Salesperson | 7 | $52,900 | |
| 312 | Afternoon | Carmelo Garcia | Detroit | Midwest | Salesperson | 29 | $36,500 | |
| 720 | Afternoon | August Garcia | Houston | South | Salesperson | 14 | $81,800 | |
| 1193 | Afternoon | Kristopher Garcia | Chicago | Midwest | Salesperson | 9 | $82,200 | |
| 1075 | Morning | Shirley Gardner | Philadelphia | East | Salesperson | 2 | $49,300 | |
| 86 | Morning | Concetta Garrison | San Diego | West | Salesperson | 11 | $77,900 | |
| 119 | Afternoon | Coleman Gates | Los Angeles | West | Salesperson | 11 | $78,400 | |
| 272 | Afternoon | Connie Gates | San Diego | West | Intern | 1 | $11,400 | |
| 495 | Morning | Tammi Gates | Phoenix | West | Salesperson | 30 | $70,000 | |
| 939 | Morning | Bettie Gates | San Diego | West | Intern | 0 | $12,500 | |
| 25 | Afternoon | Andrea Gay | Los Angeles | West | Salesperson | 26 | $84,500 | |
| 473 | Afternoon | Ali Gay | Dallas | South | Salesperson | 9 | $67,800 | |
| 105 | Afternoon | Jame Gentry | Chicago | Midwest | Salesperson | 30 | $64,100 | |
| 115 | Afternoon | Brice Gentry | New York | East | Salesperson | 10 | $46,900 | |
| 278 | Afternoon | Jacob Gentry | Dallas | South | Salesperson | 10 | $78,600 | |
| 660 | Morning | Christine Gibbs | Los Angeles | West | Salesperson | 4 | $59,500 | |
| 680 | Afternoon | Odis Gibbs | San Diego | West | Salesperson | 29 | $65,600 | |
| 404 | Morning | Elaine Giles | Dallas | South | Salesperson | 25 | $77,300 | |
| 42 | Afternoon | Guy Glass | Houston | South | Salesperson | 6 | $56,900 | |
| 676 | Afternoon | Moises Glenn | New York | East | Salesperson | 29 | $41,800 | |
| 520 | Morning | Chris Glover | Houston | South | Salesperson | 17 | $68,600 | |
| 785 | Afternoon | Vito Golden | Los Angeles | West | Salesperson | 27 | $79,200 | |
| 33 | Morning | Isabelle Gomez | New York | East | Salesperson | 20 | $48,200 | |
| 561 | Morning | Vanessa Gomez | Chicago | Midwest | Salesperson | 15 | $69,100 | |
| 631 | Afternoon | Peter Gomez | San Antonio | South | Salesperson | 22 | $38,100 | |
| 1088 | Morning | Trina Gomez | San Antonio | South | Salesperson | 5 | $75,400 | |
| 176 | Afternoon | Santiago Gonzalez | Chicago | Midwest | Salesperson | 14 | $72,500 | |
| 374 | Morning | Noreen Gonzalez | Chicago | Midwest | Salesperson | 12 | $39,900 | |
| 832 | Afternoon | Miguel Gonzalez | Detroit | Midwest | Salesperson | 19 | $65,900 | |
| 44 | Morning | Deanna Good | Phoenix | West | Salesperson | 23 | $33,400 | |
| 515 | Afternoon | Gus Goodman | San Antonio | South | Salesperson | 24 | $34,900 | |
| 861 | Afternoon | Vance Goodman | New York | East | Salesperson | 5 | $52,600 | |
| 1146 | Afternoon | Brian Goodman | San Antonio | South | Salesperson | 1 | $69,200 | |
| 164 | Morning | Melody Goodwin | Phoenix | West | Salesperson | 28 | $46,800 | |
| 214 | Morning | Rosalinda Goodwin | Phoenix | West | Salesperson | 5 | $63,400 | |
| 893 | Morning | Zelma Goodwin | Detroit | Midwest | Salesperson | 7 | $81,400 | |
| 995 | Morning | Imelda Gordon | New York | East | Salesperson | 20 | $56,300 | |
| 1063 | Afternoon | Carter Graham | San Antonio | South | Salesperson | 6 | $59,200 | |
| 153 | Morning | Michele Grant | Detroit | Midwest | Salesperson | 3 | $52,600 | |
| 458 | Morning | Gilda Grant | Detroit | Midwest | Salesperson | 17 | $36,300 | |
| 462 | Afternoon | Jon Grant | Detroit | Midwest | Salesperson | 25 | $29,600 | |
| 977 | Afternoon | Daniel Grant | Chicago | Midwest | Salesperson | 19 | $64,300 | |
| 402 | Afternoon | Malcolm Green | Los Angeles | West | Salesperson | 21 | $32,400 | |
| 917 | Afternoon | Emmett Greene | San Diego | West | Salesperson | 14 | $80,100 | |
| 987 | Afternoon | Ron Greene | Phoenix | West | Salesperson | 10 | $26,400 | |
| 429 | Morning | Betsy Greer | Phoenix | West | Salesperson | 6 | $52,300 | |
| 562 | Afternoon | Roberto Greer | Los Angeles | West | Salesperson | 20 | $43,000 | |
| 791 | Morning | Tracie Gregory | San Diego | West | Salesperson | 16 | $47,200 | |
| 1103 | Afternoon | Abraham Gregory | Los Angeles | West | Salesperson | 19 | $32,300 | |
| 89 | Afternoon | Robin Griffin | Philadelphia | East | Salesperson | 27 | $47,300 | |
| 336 | Afternoon | Elwood Griffin | Chicago | Midwest | Salesperson | 25 | $26,300 | |
| 633 | Afternoon | Blaine Grimes | Dallas | South | Salesperson | 27 | $43,800 | |
| 236 | Afternoon | Rickie Guerra | New York | East | Salesperson | 11 | $54,200 | |
| 446 | Afternoon | Ulysses Guerra | Detroit | Midwest | Salesperson | 5 | $69,600 | |
| 175 | Morning | Staci Gutierrez | San Antonio | South | Salesperson | 12 | $36,400 | |
| 889 | Morning | Shari Gutierrez | San Antonio | South | Salesperson | 4 | $68,800 | |
| 199 | Morning | Marisa Guzman | Houston | South | Salesperson | 29 | $79,900 | |
| 286 | Morning | Vicky Guzman | San Antonio | South | Salesperson | 19 | $61,800 | |
| 803 | Afternoon | Freddy Haas | Dallas | South | Salesperson | 4 | $60,500 | |
| 365 | Afternoon | Jeff Hahn | Philadelphia | East | Salesperson | 23 | $45,700 | |
| 181 | Afternoon | Lyman Hale | San Antonio | South | Salesperson | 17 | $81,400 | |
| 644 | Morning | Sharlene Hale | Detroit | Midwest | Salesperson | 30 | $25,100 | |
| 5 | Morning | Jewell Haley | Phoenix | West | Salesperson | 13 | $30,800 | |
| 263 | Morning | Imogene Hamilton | New York | East | Regional Manager | 30 | $160,000 | |
| 135 | Afternoon | Kelly Hammond | Philadelphia | East | Intern | 0 | $9,100 | |
| 314 | Afternoon | Dustin Hammond | San Antonio | South | Salesperson | 16 | $61,700 | |
| 82 | Morning | Rosella Haney | Dallas | South | Salesperson | 14 | $32,800 | |
| 159 | Afternoon | Brett Hanna | Los Angeles | West | Salesperson | 3 | $74,300 | |
| 380 | Morning | Virgie Hanna | San Antonio | South | Salesperson | 18 | $58,300 | |
| 489 | Afternoon | Fred Hanna | Philadelphia | East | Salesperson | 1 | $66,900 | |
| 248 | Afternoon | Trinidad Hansen | San Antonio | South | Salesperson | 7 | $61,500 | |
| 1000 | Morning | Roberta Hansen | Detroit | Midwest | Salesperson | 7 | $32,400 | |
| 350 | Morning | Kayla Hanson | San Antonio | South | Salesperson | 27 | $80,800 | |
| 1011 | Morning | Beverly Hanson | Chicago | Midwest | Intern | 0 | $11,000 | |
| 1109 | Morning | Carmella Hanson | Houston | South | Salesperson | 14 | $75,300 | |
| 125 | Morning | Maureen Harding | Detroit | Midwest | Salesperson | 25 | $45,700 | |
| 1132 | Afternoon | Bart Harding | Los Angeles | West | Salesperson | 24 | $27,600 | |
| 269 | Morning | Tami Hardy | Chicago | Midwest | Salesperson | 28 | $36,500 | |
| 280 | Afternoon | Kenny Hardy | Chicago | Midwest | Salesperson | 13 | $72,600 | |
| 393 | Morning | Pansy Harmon | Chicago | Midwest | Salesperson | 4 | $55,400 | |
| 554 | Morning | Suzanne Harrell | San Diego | West | Salesperson | 28 | $28,900 | |
| 766 | Afternoon | Jed Harrington | Phoenix | West | Salesperson | 15 | $58,300 | |
| 348 | Morning | Chris Harrison | Phoenix | West | Salesperson | 13 | $28,500 | |
| 736 | Morning | Jeanne Harrison | New York | East | Salesperson | 11 | $55,300 | |
| 233 | Afternoon | Zachariah Hart | Dallas | South | Salesperson | 8 | $78,300 | |
| 456 | Morning | Leonor Hart | Los Angeles | West | Salesperson | 10 | $44,500 | |
| 434 | Morning | Mai Harvey | Dallas | South | Salesperson | 2 | $49,500 | |
| 339 | Morning | Shelley Hatfield | San Diego | West | Salesperson | 3 | $65,800 | |
| 238 | Morning | Jayne Hawkins | Los Angeles | West | Salesperson | 4 | $59,100 | |
| 684 | Afternoon | Frankie Hawkins | Chicago | Midwest | Salesperson | 13 | $76,900 | |
| 962 | Morning | Betsy Hawkins | New York | East | Salesperson | 10 | $73,100 | |
| 1192 | Afternoon | Danial Hayden | San Antonio | South | Salesperson | 16 | $76,400 | |
| 802 | Afternoon | Jarrod Hayes | Houston | South | Salesperson | 10 | $27,300 | |
| 989 | Afternoon | Chadwick Hayes | Phoenix | West | Salesperson | 21 | $53,300 | |
| 259 | Morning | Denise Haynes | Houston | South | Salesperson | 26 | $42,400 | |
| 1095 | Morning | Jeanine Haynes | Philadelphia | East | Salesperson | 10 | $69,600 | |
| 841 | Afternoon | Norberto Hays | Dallas | South | Salesperson | 2 | $69,700 | |
| 330 | Afternoon | Marshall Heath | Houston | South | Salesperson | 28 | $46,500 | |
| 1167 | Afternoon | Cornelius Henderson | Chicago | Midwest | Salesperson | 10 | $38,700 | |
| 1106 | Afternoon | Thad Herman | San Diego | West | Salesperson | 1 | $74,200 | |
| 1028 | Morning | Doreen Hernandez | Philadelphia | East | Salesperson | 15 | $54,400 | |
| 755 | Afternoon | Patrick Herrera | Los Angeles | West | Salesperson | 9 | $80,400 | |
| 394 | Afternoon | Harris Herring | Dallas | South | Salesperson | 21 | $77,900 | |
| 48 | Afternoon | Dee Hess | Chicago | Midwest | Salesperson | 22 | $53,100 | |
| 261 | Morning | Leta Hester | San Diego | West | Sales Manager | 23 | $62,100 | |
| 122 | Afternoon | Pete Hicks | Dallas | South | Regional Manager | 25 | $204,000 | |
| 601 | Morning | Colleen Hicks | Chicago | Midwest | Salesperson | 19 | $54,500 | |
| 727 | Morning | Tracy Hicks | Chicago | Midwest | Salesperson | 10 | $30,600 | |
| 845 | Afternoon | Ariel Hines | Los Angeles | West | Salesperson | 12 | $50,500 | |
| 985 | Afternoon | Milton Hines | Phoenix | West | Salesperson | 3 | $74,200 | |
| 255 | Afternoon | Ned Hinton | Detroit | Midwest | Salesperson | 16 | $58,800 | |
| 1020 | Afternoon | Ezra Ho | San Diego | West | Salesperson | 19 | $45,100 | |
| 49 | Morning | Staci Hobbs | Dallas | South | Salesperson | 8 | $31,800 | |
| 30 | Afternoon | Carmine Hodge | Los Angeles | West | Salesperson | 16 | $70,900 | |
| 215 | Afternoon | Laverne Hodge | Philadelphia | East | Salesperson | 7 | $28,100 | |
| 171 | Morning | Myrtle Hoffman | Chicago | Midwest | Salesperson | 27 | $70,500 | |
| 809 | Afternoon | Max Hoffman | Phoenix | West | Salesperson | 26 | $59,800 | |
| 1026 | Morning | Marlene Hoffman | New York | East | Salesperson | 5 | $31,200 | |
| 140 | Morning | Brandie Holder | Philadelphia | East | Salesperson | 28 | $65,500 | |
| 1169 | Afternoon | Rocco Holder | Phoenix | West | Salesperson | 24 | $76,100 | |
| 597 | Morning | Marilyn Holloway | Dallas | South | Salesperson | 19 | $64,000 | |
| 166 | Morning | Dawn Holmes | New York | East | Salesperson | 20 | $64,300 | |
| 544 | Afternoon | Reynaldo Holt | San Antonio | South | Salesperson | 11 | $66,700 | |
| 422 | Morning | Rosetta Hoover | Houston | South | Salesperson | 9 | $45,100 | |
| 604 | Morning | Mavis Hoover | Los Angeles | West | Salesperson | 19 | $68,600 | |
| 193 | Afternoon | Ben Hopkins | Los Angeles | West | Salesperson | 15 | $59,500 | |
| 324 | Afternoon | Edwardo Hopkins | Phoenix | West | Salesperson | 13 | $68,000 | |
| 668 | Morning | Shelley Hopkins | San Antonio | South | Salesperson | 7 | $74,200 | |
| 715 | Morning | Ana Hopkins | Detroit | Midwest | Salesperson | 18 | $29,900 | |
| 974 | Afternoon | Todd Horn | Chicago | Midwest | Salesperson | 22 | $61,100 | |
| 1012 | Afternoon | Teddy Horn | Phoenix | West | Salesperson | 23 | $69,000 | |
| 1032 | Afternoon | Reynaldo Horne | San Diego | West | Intern | 1 | $11,500 | |
| 480 | Morning | Elnora House | Dallas | South | Salesperson | 30 | $69,900 | |
| 334 | Morning | Nikki Houston | Houston | South | Salesperson | 6 | $64,800 | |
| 874 | Afternoon | Wilton Houston | Dallas | South | Salesperson | 13 | $72,800 | |
| 24 | Morning | Deanna Howard | Chicago | Midwest | Salesperson | 30 | $55,200 | |
| 532 | Afternoon | Bud Howard | Chicago | Midwest | Salesperson | 18 | $50,900 | |
| 387 | Afternoon | Carey Howe | Chicago | Midwest | Salesperson | 1 | $57,900 | |
| 438 | Afternoon | Weston Howell | Los Angeles | West | Salesperson | 25 | $55,200 | |
| 731 | Afternoon | Antwan Howell | Phoenix | West | Intern | 0 | $9,900 | |
| 747 | Morning | Hannah Howell | Philadelphia | East | Salesperson | 15 | $59,000 | |
| 298 | Morning | Dana Huang | Los Angeles | West | Salesperson | 16 | $53,900 | |
| 412 | Morning | Lynn Huber | Phoenix | West | Salesperson | 25 | $36,200 | |
| 1001 | Afternoon | Rufus Huber | Los Angeles | West | Salesperson | 24 | $80,800 | |
| 1144 | Afternoon | Alton Huber | New York | East | Salesperson | 2 | $47,000 | |
| 11 | Afternoon | George Hudson | Dallas | South | Salesperson | 4 | $43,300 | |
| 79 | Afternoon | Mark Hudson | San Antonio | South | Salesperson | 14 | $45,600 | |
| 327 | Morning | Francis Huff | Detroit | Midwest | Salesperson | 22 | $79,600 | |
| 880 | Afternoon | Houston Huff | Detroit | Midwest | Salesperson | 11 | $76,800 | |
| 1019 | Afternoon | Fidel Hull | San Antonio | South | Salesperson | 13 | $78,200 | |
| 194 | Afternoon | Lonnie Hunt | Dallas | South | Salesperson | 18 | $71,900 | |
| 318 | Morning | Wilda Hunter | Detroit | Midwest | Salesperson | 28 | $66,500 | |
| 969 | Afternoon | Antwan Hurst | San Antonio | South | Salesperson | 28 | $34,800 | |
| 1123 | Afternoon | Garry Hurst | Detroit | Midwest | Salesperson | 26 | $75,500 | |
| 128 | Afternoon | Ryan Hutchinson | New York | East | Salesperson | 21 | $70,300 | |
| 898 | Afternoon | Ronny Hutchinson | New York | East | Salesperson | 3 | $50,200 | |
| 1004 | Morning | Christian Hutchinson | Houston | South | Salesperson | 15 | $37,500 | |
| 741 | Morning | Tammie Huynh | Detroit | Midwest | Salesperson | 24 | $53,000 | |
| 869 | Morning | Dorothea Ibarra | San Diego | West | Salesperson | 18 | $64,600 | |
| 3 | Morning | Shanna Ingram | Houston | South | Salesperson | 6 | $33,600 | |
| 915 | Morning | Tamra Ingram | Phoenix | West | Salesperson | 18 | $29,300 | |
| 940 | Afternoon | Kenneth Irwin | New York | East | Salesperson | 20 | $39,400 | |
| 1030 | Morning | Flora Irwin | Detroit | Midwest | Salesperson | 12 | $48,200 | |
| 276 | Morning | Patti Jackson | San Diego | West | Salesperson | 20 | $69,300 | |
| 516 | Afternoon | Jarred Jacobs | San Diego | West | Salesperson | 7 | $72,500 | |
| 1136 | Morning | Alyson Jacobs | Los Angeles | West | Salesperson | 7 | $26,900 | |
| 513 | Morning | Deirdre Jacobson | Dallas | South | Salesperson | 13 | $61,500 | |
| 512 | Afternoon | Jerrod James | Houston | South | Salesperson | 1 | $67,500 | |
| 1023 | Afternoon | Terrence Jarvis | Chicago | Midwest | Salesperson | 26 | $30,500 | |
| 98 | Afternoon | Derick Jefferson | New York | East | Salesperson | 28 | $71,500 | |
| 669 | Afternoon | Wilfredo Jefferson | Los Angeles | West | Salesperson | 8 | $49,200 | |
| 738 | Morning | Rosanna Jefferson | Houston | South | Salesperson | 3 | $59,400 | |
| 798 | Afternoon | Charles Jefferson | Philadelphia | East | Salesperson | 28 | $64,900 | |
| 161 | Morning | Theresa Jennings | Los Angeles | West | Salesperson | 1 | $55,500 | |
| 345 | Morning | Carey Jennings | Phoenix | West | Salesperson | 19 | $37,100 | |
| 420 | Morning | Ofelia Jennings | San Diego | West | Salesperson | 4 | $45,800 | |
| 866 | Morning | Michele Jensen | Dallas | South | Salesperson | 28 | $82,800 | |
| 109 | Morning | Marisa Johnson | San Diego | West | Salesperson | 2 | $51,300 | |
| 879 | Afternoon | Freeman Johnson | Detroit | Midwest | Salesperson | 14 | $44,300 | |
| 683 | Afternoon | Julius Jones | Chicago | Midwest | Salesperson | 12 | $68,400 | |
| 1046 | Morning | Brandie Jordan | New York | East | Salesperson | 26 | $78,100 | |
| 423 | Afternoon | Cliff Joseph | San Diego | West | Salesperson | 3 | $34,700 | |
| 396 | Afternoon | Thanh Joyce | New York | East | Salesperson | 11 | $32,500 | |
| 323 | Morning | Erika Kaiser | Phoenix | West | Salesperson | 3 | $62,200 | |
| 1044 | Afternoon | Vince Kaiser | Phoenix | West | Salesperson | 11 | $59,600 | |
| 108 | Afternoon | Forrest Kane | Philadelphia | East | Salesperson | 22 | $75,500 | |
| 829 | Morning | Mavis Kane | Detroit | Midwest | Salesperson | 11 | $60,400 | |
| 459 | Morning | Jacklyn Kaufman | Detroit | Midwest | Salesperson | 6 | $31,500 | |
| 414 | Afternoon | Teddy Keller | Houston | South | Salesperson | 4 | $29,300 | |
| 421 | Morning | Anne Kelley | New York | East | Salesperson | 28 | $70,800 | |
| 242 | Afternoon | Bart Kelly | Houston | South | Salesperson | 25 | $75,700 | |
| 343 | Morning | Carla Kelly | San Diego | West | Salesperson | 3 | $54,300 | |
| 436 | Afternoon | Irving Kelly | Los Angeles | West | Salesperson | 18 | $25,500 | |
| 1056 | Morning | Gertrude Kelly | Chicago | Midwest | Salesperson | 8 | $48,100 | |
| 81 | Afternoon | Quincy Kennedy | Detroit | Midwest | Salesperson | 11 | $35,200 | |
| 303 | Morning | Sheryl Kennedy | Dallas | South | Salesperson | 1 | $83,000 | |
| 1174 | Morning | Veronica Kent | San Antonio | South | Salesperson | 4 | $63,600 | |
| 229 | Afternoon | Thanh Kerr | Los Angeles | West | Salesperson | 23 | $55,700 | |
| 699 | Afternoon | Roscoe Kidd | Los Angeles | West | Salesperson | 3 | $41,600 | |
| 765 | Afternoon | Esteban Kidd | Philadelphia | East | Salesperson | 9 | $62,500 | |
| 768 | Afternoon | Chester Kidd | Philadelphia | East | Salesperson | 10 | $84,100 | |
| 945 | Afternoon | Carl Kidd | Houston | South | Salesperson | 11 | $55,500 | |
| 946 | Morning | Elsie Kim | Houston | South | Salesperson | 13 | $83,500 | |
| 555 | Afternoon | Omar King | Los Angeles | West | Salesperson | 4 | $47,600 | |
| 714 | Afternoon | Osvaldo King | San Antonio | South | Salesperson | 10 | $62,400 | |
| 848 | Afternoon | Tyrone King | San Diego | West | Salesperson | 29 | $76,100 | |
| 302 | Afternoon | Bobby Kirk | San Antonio | South | Salesperson | 19 | $80,700 | |
| 451 | Morning | Lenora Klein | Houston | South | Salesperson | 27 | $26,100 | |
| 767 | Afternoon | Marcel Klein | Philadelphia | East | Salesperson | 28 | $29,300 | |
| 478 | Afternoon | John Kline | San Diego | West | Salesperson | 9 | $38,700 | |
| 530 | Afternoon | Mauricio Kline | Chicago | Midwest | Salesperson | 21 | $42,400 | |
| 437 | Afternoon | Kenton Knight | San Diego | West | Salesperson | 15 | $63,800 | |
| 549 | Morning | Luella Knight | Detroit | Midwest | Salesperson | 18 | $33,800 | |
| 158 | Afternoon | Julius Knox | Chicago | Midwest | Salesperson | 23 | $52,700 | |
| 168 | Afternoon | Isaac Knox | Houston | South | Salesperson | 8 | $76,100 | |
| 794 | Morning | Chandra Knox | Los Angeles | West | Salesperson | 19 | $43,800 | |
| 525 | Afternoon | Rickie Kramer | Los Angeles | West | Salesperson | 26 | $56,400 | |
| 725 | Morning | Savannah Krause | Chicago | Midwest | Salesperson | 7 | $61,200 | |
| 745 | Afternoon | Monte Krause | Chicago | Midwest | Salesperson | 26 | $41,000 | |
| 547 | Afternoon | Johnie Krueger | Detroit | Midwest | Salesperson | 11 | $34,900 | |
| 760 | Afternoon | Rusty Krueger | Dallas | South | Salesperson | 16 | $25,800 | |
| 1008 | Morning | Jannie Krueger | New York | East | Salesperson | 1 | $36,900 | |
| 553 | Morning | Ruthie Lam | Dallas | South | Salesperson | 25 | $66,800 | |
| 786 | Afternoon | Wm Lamb | Houston | South | Salesperson | 4 | $42,800 | |
| 1098 | Afternoon | Everette Lamb | Philadelphia | East | Salesperson | 18 | $81,100 | |
| 574 | Afternoon | Frankie Landry | Chicago | Midwest | Salesperson | 9 | $54,900 | |
| 581 | Morning | Alexandra Landry | San Diego | West | Salesperson | 4 | $44,100 | |
| 751 | Afternoon | Enrique Lang | Phoenix | West | Salesperson | 8 | $38,200 | |
| 1100 | Morning | Barbra Lang | Philadelphia | East | Salesperson | 26 | $51,700 | |
| 68 | Afternoon | Prince Lawrence | Detroit | Midwest | Salesperson | 24 | $54,500 | |
| 444 | Afternoon | Glen Lawrence | Los Angeles | West | Intern | 0 | $11,600 | |
| 260 | Morning | Marisa Le | Chicago | Midwest | Salesperson | 30 | $75,700 | |
| 501 | Afternoon | Jean Leach | Dallas | South | Salesperson | 18 | $34,800 | |
| 629 | Morning | Christa Leach | Dallas | South | Salesperson | 1 | $41,400 | |
| 705 | Afternoon | Danial Leblanc | Houston | South | Salesperson | 29 | $72,300 | |
| 1172 | Morning | Dollie Lee | Phoenix | West | Salesperson | 7 | $67,200 | |
| 195 | Afternoon | Jacob Leon | San Antonio | South | Salesperson | 29 | $43,700 | |
| 882 | Morning | Katy Lester | Detroit | Midwest | Salesperson | 8 | $26,900 | |
| 606 | Afternoon | Mitchel Levine | Chicago | Midwest | Salesperson | 13 | $52,000 | |
| 376 | Morning | Leigh Levy | San Antonio | South | Salesperson | 8 | $44,000 | |
| 417 | Afternoon | Daren Levy | Phoenix | West | Salesperson | 23 | $29,900 | |
| 534 | Afternoon | Marc Levy | Detroit | Midwest | Salesperson | 8 | $45,500 | |
| 1031 | Afternoon | Elwood Levy | Detroit | Midwest | Salesperson | 1 | $77,700 | |
| 277 | Afternoon | Jared Lewis | Chicago | Midwest | Salesperson | 23 | $39,600 | |
| 674 | Afternoon | Sean Lewis | Dallas | South | Salesperson | 17 | $51,100 | |
| 847 | Afternoon | Everette Lin | San Antonio | South | Salesperson | 15 | $68,900 | |
| 22 | Afternoon | George Lindsey | Detroit | Midwest | Salesperson | 19 | $80,900 | |
| 180 | Afternoon | Bennett Lindsey | Phoenix | West | Salesperson | 10 | $56,000 | |
| 127 | Morning | Jessie Little | Houston | South | Salesperson | 14 | $58,800 | |
| 253 | Morning | Juliette Little | Chicago | Midwest | Salesperson | 13 | $80,500 | |
| 1134 | Afternoon | Stevie Little | Dallas | South | Salesperson | 14 | $72,200 | |
| 132 | Afternoon | Norberto Liu | Chicago | Midwest | Salesperson | 26 | $61,800 | |
| 519 | Afternoon | Cameron Liu | San Antonio | South | Salesperson | 19 | $77,800 | |
| 782 | Afternoon | Rico Liu | San Diego | West | Salesperson | 12 | $63,400 | |
| 1126 | Morning | Angeline Liu | Houston | South | Salesperson | 28 | $65,700 | |
| 273 | Morning | Priscilla Livingston | Dallas | South | Intern | 0 | $6,200 | |
| 419 | Afternoon | Moises Livingston | Houston | South | Salesperson | 28 | $60,100 | |
| 735 | Afternoon | Wilfred Livingston | Los Angeles | West | Salesperson | 16 | $64,900 | |
| 822 | Afternoon | Clement Lloyd | Philadelphia | East | Intern | 1 | $14,400 | |
| 20 | Afternoon | Julius Logan | New York | East | Salesperson | 24 | $29,000 | |
| 274 | Afternoon | Kristopher Logan | Detroit | Midwest | Salesperson | 10 | $51,600 | |
| 739 | Afternoon | Howard Logan | Houston | South | Salesperson | 11 | $54,800 | |
| 378 | Morning | Margery Love | Dallas | South | Salesperson | 13 | $69,500 | |
| 611 | Morning | Rhoda Love | Philadelphia | East | Salesperson | 12 | $82,500 | |
| 121 | Afternoon | Phillip Lucas | Los Angeles | West | Salesperson | 22 | $45,300 | |
| 1033 | Morning | Lorena Lucas | San Diego | West | Salesperson | 23 | $72,800 | |
| 385 | Afternoon | Williams Luna | New York | East | Salesperson | 1 | $75,700 | |
| 955 | Morning | Marquita Luna | San Antonio | South | Salesperson | 23 | $57,700 | |
| 9 | Afternoon | Joey Lynn | Houston | South | Salesperson | 8 | $41,100 | |
| 157 | Afternoon | Gene Lynn | Phoenix | West | Salesperson | 22 | $80,600 | |
| 67 | Morning | Lessie Lyons | Phoenix | West | Salesperson | 11 | $74,200 | |
| 340 | Morning | Reba Lyons | New York | East | Salesperson | 17 | $44,500 | |
| 572 | Morning | Liz Lyons | New York | East | Salesperson | 24 | $81,800 | |
| 713 | Morning | Judith Lyons | Phoenix | West | Salesperson | 24 | $47,500 | |
| 443 | Afternoon | Dino Macdonald | Phoenix | West | Salesperson | 12 | $78,300 | |
| 74 | Afternoon | Murray Macias | Dallas | South | Salesperson | 30 | $50,800 | |
| 178 | Afternoon | Vicente Macias | San Antonio | South | Salesperson | 30 | $77,700 | |
| 409 | Morning | Janie Macias | Dallas | South | Salesperson | 6 | $75,300 | |
| 257 | Afternoon | Joe Maddox | Detroit | Midwest | Salesperson | 29 | $31,000 | |
| 183 | Morning | Beverley Mahoney | Phoenix | West | Salesperson | 15 | $29,800 | |
| 497 | Morning | Corinne Maldonado | Phoenix | West | Salesperson | 7 | $43,200 | |
| 901 | Afternoon | Ramon Maldonado | Philadelphia | East | Salesperson | 13 | $61,500 | |
| 936 | Morning | Marietta Maldonado | Los Angeles | West | Salesperson | 12 | $68,300 | |
| 1120 | Afternoon | Raphael Maldonado | Philadelphia | East | Salesperson | 6 | $56,700 | |
| 1188 | Morning | Ronda Malone | New York | East | Intern | 1 | $13,000 | |
| 21 | Afternoon | Abel Marks | San Diego | West | Salesperson | 7 | $46,100 | |
| 218 | Morning | Adela Marks | New York | East | Salesperson | 7 | $34,500 | |
| 526 | Afternoon | Kendrick Marks | Philadelphia | East | Salesperson | 19 | $63,700 | |
| 16 | Morning | Barbara Marquez | Los Angeles | West | Salesperson | 9 | $76,100 | |
| 909 | Afternoon | Bradley Marquez | Philadelphia | East | Salesperson | 30 | $57,300 | |
| 671 | Morning | Bernadine Marshall | Los Angeles | West | Salesperson | 29 | $45,900 | |
| 207 | Morning | Helena Mason | Phoenix | West | Salesperson | 21 | $73,300 | |
| 916 | Afternoon | Merle Mason | Chicago | Midwest | Salesperson | 7 | $69,300 | |
| 91 | Morning | Reyna Massey | Dallas | South | Salesperson | 22 | $67,300 | |
| 771 | Afternoon | Shane Mathis | Houston | South | Salesperson | 20 | $49,800 | |
| 920 | Morning | Katelyn Mathis | Phoenix | West | Salesperson | 13 | $41,300 | |
| 331 | Afternoon | Isidro Matthews | San Antonio | South | Salesperson | 11 | $51,000 | |
| 453 | Morning | Peggy Matthews | Philadelphia | East | Salesperson | 6 | $67,300 | |
| 627 | Afternoon | Gene Matthews | Dallas | South | Salesperson | 26 | $55,300 | |
| 774 | Afternoon | Donald Matthews | San Antonio | South | Salesperson | 5 | $73,400 | |
| 1102 | Morning | Marsha Maxwell | Detroit | Midwest | Salesperson | 29 | $83,900 | |
| 712 | Morning | Frieda May | Phoenix | West | Salesperson | 27 | $37,800 | |
| 965 | Afternoon | Mathew May | Houston | South | Salesperson | 5 | $73,100 | |
| 496 | Afternoon | Armand Mayer | New York | East | Salesperson | 29 | $66,100 | |
| 679 | Afternoon | Lucio Mayer | Chicago | Midwest | Salesperson | 15 | $51,800 | |
| 717 | Afternoon | Santiago Mayer | Phoenix | West | Salesperson | 20 | $68,900 | |
| 677 | Morning | Andrea Maynard | Houston | South | Salesperson | 14 | $63,400 | |
| 763 | Morning | Kristy Mays | Chicago | Midwest | Salesperson | 18 | $75,400 | |
| 1197 | Morning | Gale Mays | Dallas | South | Salesperson | 29 | $28,100 | |
| 191 | Morning | Natasha Mccann | New York | East | Salesperson | 23 | $42,200 | |
| 371 | Morning | Mari Mccarty | Los Angeles | West | Salesperson | 28 | $26,500 | |
| 711 | Morning | Zelma Mcconnell | Houston | South | Salesperson | 22 | $35,800 | |
| 890 | Afternoon | Sherman Mcconnell | San Diego | West | Salesperson | 7 | $51,900 | |
| 1034 | Morning | Deanna Mcconnell | San Antonio | South | Salesperson | 14 | $41,100 | |
| 1055 | Morning | Rose Mccormick | Philadelphia | East | Salesperson | 29 | $85,000 | |
| 628 | Morning | Sonya Mccoy | San Antonio | South | Salesperson | 15 | $67,500 | |
| 227 | Morning | Herminia Mccullough | Chicago | Midwest | Salesperson | 4 | $33,800 | |
| 390 | Afternoon | Isaiah Mcdaniel | New York | East | Salesperson | 10 | $49,900 | |
| 796 | Morning | Virginia Mcdaniel | Los Angeles | West | Salesperson | 16 | $62,900 | |
| 120 | Afternoon | Bert Mcdonald | New York | East | Sales Manager | 18 | $108,900 | |
| 834 | Morning | Tina Mcdonald | Dallas | South | Salesperson | 20 | $51,400 | |
| 1111 | Morning | Samantha Mcdonald | Dallas | South | Salesperson | 10 | $49,800 | |
| 145 | Morning | Bridget Mcdowell | San Antonio | South | Salesperson | 10 | $66,500 | |
| 138 | Morning | Sandra Mcfarland | San Antonio | South | Salesperson | 25 | $44,900 | |
| 910 | Afternoon | Salvador Mcfarland | San Antonio | South | Salesperson | 12 | $56,500 | |
| 521 | Afternoon | Ryan Mcgee | San Diego | West | Salesperson | 5 | $82,700 | |
| 836 | Morning | Rose Mcgee | New York | East | Salesperson | 3 | $29,100 | |
| 958 | Afternoon | Eugenio Mcgee | San Antonio | South | Salesperson | 11 | $46,100 | |
| 1119 | Morning | Nadine Mcgrath | New York | East | Salesperson | 18 | $40,500 | |
| 52 | Afternoon | Royce Mcintosh | Houston | South | Salesperson | 2 | $32,700 | |
| 1054 | Morning | Terra Mcintosh | Chicago | Midwest | Salesperson | 11 | $39,100 | |
| 1187 | Morning | Cherie Mcintosh | Los Angeles | West | Salesperson | 7 | $41,300 | |
| 773 | Afternoon | Burl Mcintyre | Dallas | South | Salesperson | 27 | $80,900 | |
| 1068 | Afternoon | Roger Mckee | New York | East | Salesperson | 7 | $36,100 | |
| 1200 | Morning | Margret Mckee | Dallas | South | Salesperson | 11 | $77,400 | |
| 235 | Morning | Paulette Mckenzie | San Diego | West | Salesperson | 8 | $28,700 | |
| 256 | Afternoon | Mathew Mckenzie | Los Angeles | West | Salesperson | 17 | $72,800 | |
| 377 | Morning | Pauline Mcknight | Dallas | South | Salesperson | 5 | $80,700 | |
| 1173 | Afternoon | Fredric Mcknight | Detroit | Midwest | Salesperson | 19 | $36,400 | |
| 80 | Morning | Juliet Mcmahon | San Diego | West | Salesperson | 30 | $32,200 | |
| 352 | Morning | Latisha Mcmahon | Chicago | Midwest | Salesperson | 24 | $39,000 | |
| 433 | Afternoon | William Mcmillan | Philadelphia | East | Salesperson | 16 | $72,000 | |
| 1086 | Morning | Terrie Mcmillan | Detroit | Midwest | Salesperson | 26 | $83,100 | |
| 224 | Morning | Ebony Mcneil | Detroit | Midwest | Salesperson | 8 | $50,300 | |
| 435 | Afternoon | Jordan Mcneil | New York | East | Salesperson | 2 | $57,000 | |
| 687 | Afternoon | Cary Mcneil | New York | East | Intern | 0 | $11,400 | |
| 775 | Morning | Reyna Mcpherson | Houston | South | Salesperson | 1 | $47,900 | |
| 476 | Afternoon | Bart Medina | New York | East | Salesperson | 4 | $63,800 | |
| 787 | Afternoon | Kennith Medina | New York | East | Salesperson | 11 | $47,200 | |
| 76 | Morning | Catalina Mejia | Philadelphia | East | Sales Manager | 30 | $86,700 | |
| 250 | Afternoon | Thurman Mejia | Los Angeles | West | Salesperson | 22 | $66,300 | |
| 332 | Morning | Laura Mejia | Chicago | Midwest | Salesperson | 2 | $44,700 | |
| 780 | Morning | Melissa Mejia | Los Angeles | West | Salesperson | 27 | $68,500 | |
| 860 | Afternoon | Alexander Mejia | San Antonio | South | Salesperson | 4 | $34,900 | |
| 424 | Afternoon | Daren Melton | Los Angeles | West | Salesperson | 8 | $81,600 | |
| 470 | Morning | Alisa Melton | Chicago | Midwest | Salesperson | 24 | $82,400 | |
| 511 | Afternoon | Derrick Melton | San Antonio | South | Salesperson | 6 | $68,100 | |
| 528 | Morning | Melanie Melton | Detroit | Midwest | Salesperson | 3 | $67,600 | |
| 1084 | Morning | Shelby Mendez | Phoenix | West | Salesperson | 7 | $44,200 | |
| 1047 | Afternoon | Russ Mendoza | Philadelphia | East | Salesperson | 23 | $55,900 | |
| 1092 | Morning | Bertha Mendoza | Philadelphia | East | Salesperson | 15 | $47,900 | |
| 1168 | Morning | Debra Mercer | San Antonio | South | Salesperson | 1 | $39,200 | |
| 840 | Morning | Kay Meyer | Los Angeles | West | Salesperson | 22 | $56,100 | |
| 57 | Morning | Sheree Meyers | Dallas | South | Salesperson | 9 | $40,100 | |
| 244 | Afternoon | Chase Middleton | Detroit | Midwest | Salesperson | 23 | $28,800 | |
| 1171 | Afternoon | Jules Middleton | Dallas | South | Salesperson | 5 | $41,500 | |
| 982 | Morning | Vanessa Miles | Detroit | Midwest | Salesperson | 15 | $42,700 | |
| 264 | Morning | Stacey Mitchell | New York | East | Salesperson | 4 | $29,000 | |
| 864 | Afternoon | Preston Mitchell | Detroit | Midwest | Salesperson | 26 | $69,200 | |
| 944 | Afternoon | Sydney Mitchell | Phoenix | West | Salesperson | 16 | $44,900 | |
| 728 | Afternoon | Jacques Molina | Phoenix | West | Salesperson | 17 | $52,700 | |
| 1097 | Afternoon | Burton Molina | Los Angeles | West | Salesperson | 17 | $77,800 | |
| 202 | Afternoon | Vaughn Monroe | San Antonio | South | Salesperson | 16 | $47,900 | |
| 220 | Afternoon | Alva Monroe | Dallas | South | Salesperson | 11 | $25,500 | |
| 870 | Afternoon | Ross Monroe | Los Angeles | West | Salesperson | 30 | $71,300 | |
| 223 | Morning | Justine Montes | New York | East | Salesperson | 17 | $83,300 | |
| 800 | Afternoon | Dirk Montes | Houston | South | Salesperson | 10 | $64,600 | |
| 342 | Afternoon | Jasper Montgomery | New York | East | Salesperson | 26 | $74,500 | |
| 641 | Morning | Minerva Montgomery | San Diego | West | Salesperson | 29 | $27,300 | |
| 806 | Morning | Tisha Montgomery | Phoenix | West | Salesperson | 12 | $73,400 | |
| 876 | Morning | Jeannine Montoya | Philadelphia | East | Salesperson | 24 | $34,800 | |
| 963 | Morning | Isabel Montoya | Phoenix | West | Salesperson | 21 | $75,800 | |
| 968 | Morning | Terra Montoya | Houston | South | Salesperson | 20 | $29,800 | |
| 942 | Morning | Elisabeth Moody | Chicago | Midwest | Salesperson | 16 | $32,700 | |
| 959 | Morning | Carmella Moon | Phoenix | West | Salesperson | 1 | $49,000 | |
| 1198 | Afternoon | Landon Mooney | Philadelphia | East | Salesperson | 21 | $26,400 | |
| 661 | Afternoon | Luciano Moore | San Diego | West | Salesperson | 23 | $36,600 | |
| 682 | Afternoon | Mervin Moore | Houston | South | Salesperson | 28 | $61,200 | |
| 858 | Afternoon | Jamar Moore | San Antonio | South | Salesperson | 18 | $80,700 | |
| 7 | Morning | Marisa Mora | Philadelphia | East | Salesperson | 10 | $69,500 | |
| 502 | Afternoon | Theron Morales | New York | East | Salesperson | 3 | $76,400 | |
| 662 | Afternoon | Lucio Morales | Chicago | Midwest | Salesperson | 9 | $44,300 | |
| 842 | Afternoon | Cody Moreno | Detroit | Midwest | Salesperson | 3 | $35,900 | |
| 19 | Afternoon | Moses Morgan | Chicago | Midwest | Salesperson | 20 | $58,300 | |
| 1093 | Morning | Lupe Morgan | San Diego | West | Salesperson | 2 | $50,300 | |
| 912 | Morning | Bette Morris | Los Angeles | West | Salesperson | 23 | $26,500 | |
| 222 | Morning | Anna Morrow | Phoenix | West | Salesperson | 16 | $83,500 | |
| 846 | Morning | Charmaine Morrow | Chicago | Midwest | Salesperson | 17 | $36,900 | |
| 1115 | Afternoon | Alvin Morrow | Phoenix | West | Salesperson | 19 | $44,400 | |
| 577 | Afternoon | Truman Morse | San Diego | West | Salesperson | 13 | $76,000 | |
| 664 | Morning | Imogene Morse | Los Angeles | West | Salesperson | 17 | $30,300 | |
| 17 | Afternoon | Erwin Mosley | Dallas | South | Salesperson | 10 | $50,300 | |
| 1080 | Morning | Tasha Mosley | Dallas | South | Salesperson | 22 | $82,100 | |
| 94 | Afternoon | Shelby Moss | Los Angeles | West | Salesperson | 3 | $27,200 | |
| 1133 | Afternoon | Alphonse Moss | Phoenix | West | Salesperson | 30 | $40,700 | |
| 653 | Morning | Louisa Mueller | New York | East | Salesperson | 18 | $28,700 | |
| 655 | Afternoon | Morris Mueller | Los Angeles | West | Salesperson | 20 | $82,400 | |
| 349 | Afternoon | Bryon Mullins | Chicago | Midwest | Salesperson | 24 | $80,200 | |
| 468 | Afternoon | Elliott Mullins | Phoenix | West | Salesperson | 19 | $62,900 | |
| 558 | Morning | Hester Munoz | Chicago | Midwest | Salesperson | 26 | $59,000 | |
| 618 | Morning | Wilda Munoz | San Diego | West | Salesperson | 27 | $26,100 | |
| 746 | Afternoon | Marlin Munoz | Los Angeles | West | Salesperson | 24 | $53,900 | |
| 781 | Morning | Jill Murillo | Houston | South | Salesperson | 11 | $46,200 | |
| 892 | Morning | Corinne Murillo | San Diego | West | Salesperson | 6 | $53,000 | |
| 733 | Afternoon | Jarrett Murphy | San Diego | West | Salesperson | 19 | $81,200 | |
| 392 | Morning | Cecelia Murray | Chicago | Midwest | Salesperson | 5 | $50,600 | |
| 461 | Afternoon | Derick Navarro | Houston | South | Salesperson | 25 | $81,800 | |
| 531 | Morning | Cora Navarro | Los Angeles | West | Salesperson | 15 | $50,400 | |
| 613 | Morning | Kari Navarro | New York | East | Salesperson | 4 | $71,200 | |
| 143 | Afternoon | Monte Neal | Philadelphia | East | Salesperson | 27 | $51,700 | |
| 576 | Afternoon | Arturo Neal | San Antonio | South | Salesperson | 8 | $82,600 | |
| 1157 | Afternoon | Phil Neal | New York | East | Salesperson | 2 | $39,500 | |
| 296 | Morning | Janice Nelson | Chicago | Midwest | Salesperson | 12 | $70,900 | |
| 971 | Afternoon | Ivan Newman | Dallas | South | Salesperson | 15 | $64,800 | |
| 1128 | Afternoon | Blaine Newman | San Diego | West | Intern | 1 | $12,700 | |
| 566 | Afternoon | Brett Newton | Dallas | South | Salesperson | 11 | $75,100 | |
| 863 | Afternoon | Joaquin Nguyen | Detroit | Midwest | Salesperson | 12 | $33,900 | |
| 1027 | Morning | Charmaine Nguyen | San Diego | West | Salesperson | 28 | $41,700 | |
| 333 | Morning | Rene Nichols | Los Angeles | West | Salesperson | 18 | $72,800 | |
| 1076 | Afternoon | Josef Nicholson | San Antonio | South | Salesperson | 6 | $53,400 | |
| 1061 | Afternoon | Edwin Nielsen | Chicago | Midwest | Salesperson | 16 | $46,600 | |
| 626 | Afternoon | Dallas Nixon | San Antonio | South | Salesperson | 14 | $53,200 | |
| 107 | Afternoon | John Noble | Detroit | Midwest | Salesperson | 3 | $75,400 | |
| 8 | Afternoon | Russell Nolan | Philadelphia | East | Salesperson | 13 | $58,300 | |
| 55 | Afternoon | Tristan Nolan | Detroit | Midwest | Salesperson | 14 | $41,300 | |
| 103 | Morning | Shari Nolan | Houston | South | Salesperson | 17 | $41,000 | |
| 1135 | Afternoon | Lucien Nolan | Los Angeles | West | Salesperson | 3 | $43,200 | |
| 249 | Afternoon | Jason Norman | Phoenix | West | Salesperson | 12 | $64,800 | |
| 439 | Afternoon | Sidney Norris | Houston | South | Salesperson | 18 | $81,100 | |
| 1189 | Afternoon | Truman Novak | Los Angeles | West | Salesperson | 16 | $51,200 | |
| 1124 | Morning | Ada Nunez | Dallas | South | Salesperson | 24 | $30,800 | |
| 104 | Afternoon | Kennith Obrien | Philadelphia | East | Salesperson | 20 | $58,900 | |
| 353 | Morning | May Obrien | Detroit | Midwest | Salesperson | 12 | $33,600 | |
| 632 | Morning | Lavonne Obrien | San Antonio | South | Salesperson | 18 | $51,000 | |
| 642 | Afternoon | Nigel Obrien | Los Angeles | West | Salesperson | 25 | $33,900 | |
| 813 | Afternoon | Geoffrey Ochoa | Los Angeles | West | Salesperson | 25 | $52,500 | |
| 228 | Morning | Connie Oconnor | New York | East | Salesperson | 11 | $69,100 | |
| 410 | Morning | Regina Odom | Philadelphia | East | Salesperson | 27 | $29,000 | |
| 1148 | Morning | Dona Odom | New York | East | Salesperson | 27 | $83,500 | |
| 54 | Afternoon | Kenton Odonnell | Dallas | South | Salesperson | 2 | $64,300 | |
| 702 | Morning | Lillie Odonnell | Los Angeles | West | Salesperson | 6 | $32,100 | |
| 279 | Morning | Haley Oliver | Chicago | Midwest | Salesperson | 23 | $54,200 | |
| 905 | Afternoon | Randell Oliver | Chicago | Midwest | Salesperson | 12 | $47,100 | |
| 59 | Morning | Ronda Olsen | Philadelphia | East | Salesperson | 5 | $45,600 | |
| 134 | Afternoon | Hung Olson | New York | East | Salesperson | 4 | $83,300 | |
| 201 | Morning | Belinda Olson | San Diego | West | Salesperson | 1 | $29,200 | |
| 366 | Morning | Jana Olson | Chicago | Midwest | Salesperson | 23 | $29,700 | |
| 827 | Morning | Doris Olson | San Antonio | South | Salesperson | 19 | $37,800 | |
| 1085 | Afternoon | Olen Olson | Phoenix | West | Salesperson | 17 | $73,600 | |
| 1112 | Afternoon | Bart Oneill | Detroit | Midwest | Salesperson | 2 | $65,000 | |
| 950 | Morning | Lorrie Ortega | San Antonio | South | Salesperson | 23 | $65,500 | |
| 186 | Morning | Jolene Ortiz | Phoenix | West | Salesperson | 3 | $67,900 | |
| 570 | Morning | Annette Ortiz | San Diego | West | Salesperson | 12 | $46,200 | |
| 610 | Morning | Kathrine Ortiz | Philadelphia | East | Salesperson | 27 | $74,400 | |
| 899 | Morning | Melody Osborn | Los Angeles | West | Salesperson | 25 | $47,900 | |
| 906 | Afternoon | Santos Osborn | Detroit | Midwest | Salesperson | 14 | $53,400 | |
| 452 | Afternoon | Daren Osborne | Houston | South | Salesperson | 3 | $46,600 | |
| 992 | Afternoon | Ian Osborne | San Antonio | South | Salesperson | 14 | $82,900 | |
| 1162 | Afternoon | Jeffery Osborne | Los Angeles | West | Salesperson | 1 | $70,500 | |
| 450 | Morning | Lilian Owen | New York | East | Salesperson | 4 | $80,300 | |
| 221 | Morning | Kris Owens | Houston | South | Salesperson | 18 | $82,000 | |
| 770 | Morning | Ericka Owens | Philadelphia | East | Salesperson | 17 | $62,400 | |
| 873 | Afternoon | Linwood Owens | Los Angeles | West | Salesperson | 9 | $29,500 | |
| 209 | Morning | Shelby Pace | Dallas | South | Salesperson | 21 | $78,300 | |
| 326 | Afternoon | Walter Pacheco | San Diego | West | Salesperson | 4 | $73,100 | |
| 640 | Afternoon | Garrett Pacheco | San Antonio | South | Salesperson | 22 | $59,200 | |
| 10 | Afternoon | Isiah Padilla | New York | East | Salesperson | 1 | $47,300 | |
| 1016 | Morning | Allyson Page | Detroit | Midwest | Salesperson | 24 | $84,600 | |
| 603 | Morning | Renee Palmer | Philadelphia | East | Salesperson | 1 | $29,200 | |
| 156 | Afternoon | Cornelius Parker | Houston | South | Salesperson | 25 | $78,600 | |
| 198 | Morning | Alyssa Parker | Phoenix | West | Salesperson | 13 | $63,000 | |
| 370 | Afternoon | Neal Parker | Philadelphia | East | Salesperson | 15 | $43,300 | |
| 1048 | Afternoon | Ruben Parker | Houston | South | Intern | 0 | $6,200 | |
| 411 | Morning | Janell Parks | Phoenix | West | Salesperson | 20 | $37,600 | |
| 886 | Afternoon | Randal Parks | New York | East | Salesperson | 8 | $59,300 | |
| 1024 | Morning | Mariana Patrick | Houston | South | Salesperson | 10 | $84,100 | |
| 933 | Afternoon | Roger Patterson | Houston | South | Salesperson | 22 | $36,600 | |
| 1125 | Afternoon | Josef Patterson | Houston | South | Salesperson | 27 | $50,200 | |
| 187 | Afternoon | Carol Payne | Houston | South | Salesperson | 11 | $28,800 | |
| 464 | Afternoon | Alvaro Pearson | San Diego | West | Salesperson | 5 | $72,100 | |
| 769 | Afternoon | Courtney Pearson | Dallas | South | Salesperson | 13 | $84,800 | |
| 149 | Afternoon | Emerson Pena | San Antonio | South | Salesperson | 1 | $52,900 | |
| 292 | Afternoon | Forest Perez | Chicago | Midwest | Salesperson | 23 | $75,100 | |
| 730 | Afternoon | Merle Perez | San Antonio | South | Salesperson | 27 | $48,700 | |
| 931 | Afternoon | Bryon Perez | Philadelphia | East | Intern | 0 | $10,400 | |
| 118 | Afternoon | Lorenzo Perkins | Detroit | Midwest | Salesperson | 26 | $78,700 | |
| 1043 | Morning | Lara Perkins | Houston | South | Salesperson | 15 | $25,200 | |
| 130 | Afternoon | Jacques Perry | Chicago | Midwest | Salesperson | 18 | $66,400 | |
| 722 | Afternoon | Stefan Peters | Houston | South | Salesperson | 19 | $56,600 | |
| 1049 | Afternoon | Colby Peters | Los Angeles | West | Salesperson | 29 | $33,400 | |
| 363 | Afternoon | Johnathon Petersen | Phoenix | West | Salesperson | 29 | $72,500 | |
| 928 | Morning | Joy Petersen | Houston | South | Salesperson | 9 | $57,300 | |
| 1177 | Morning | Imelda Peterson | San Antonio | South | Salesperson | 24 | $28,600 | |
| 254 | Afternoon | Seymour Petty | Los Angeles | West | Salesperson | 10 | $69,000 | |
| 918 | Afternoon | Brenton Pham | Los Angeles | West | Salesperson | 9 | $46,200 | |
| 494 | Afternoon | Reinaldo Phelps | Dallas | South | Salesperson | 15 | $46,700 | |
| 551 | Morning | Cathy Phelps | Los Angeles | West | Salesperson | 16 | $30,600 | |
| 36 | Morning | Robin Phillips | Los Angeles | West | Salesperson | 20 | $32,400 | |
| 317 | Afternoon | Lewis Pierce | Detroit | Midwest | Intern | 1 | $10,200 | |
| 578 | Morning | Bertie Pierce | San Diego | West | Salesperson | 26 | $28,900 | |
| 211 | Morning | Laurie Pineda | New York | East | Salesperson | 8 | $53,500 | |
| 536 | Afternoon | Jordan Pineda | Chicago | Midwest | Salesperson | 3 | $40,800 | |
| 383 | Morning | Theresa Pittman | Detroit | Midwest | Salesperson | 21 | $37,100 | |
| 776 | Afternoon | Jonathan Ponce | Los Angeles | West | Salesperson | 11 | $53,300 | |
| 855 | Afternoon | Lorenzo Ponce | Philadelphia | East | Salesperson | 8 | $25,200 | |
| 389 | Morning | Viola Pope | Houston | South | Salesperson | 17 | $74,900 | |
| 556 | Morning | Mia Pope | Phoenix | West | Salesperson | 8 | $78,100 | |
| 758 | Morning | Ashlee Porter | Los Angeles | West | Salesperson | 7 | $28,300 | |
| 1090 | Afternoon | Noel Potter | Philadelphia | East | Salesperson | 30 | $58,700 | |
| 65 | Morning | Kathrine Potts | Los Angeles | West | Salesperson | 4 | $47,300 | |
| 63 | Morning | Rosalinda Powell | Houston | South | Salesperson | 24 | $83,300 | |
| 188 | Morning | Kaye Powell | Dallas | South | Salesperson | 3 | $34,500 | |
| 690 | Afternoon | Julius Powell | Phoenix | West | Salesperson | 25 | $65,700 | |
| 6 | Morning | Clarice Powers | Chicago | Midwest | Salesperson | 29 | $65,000 | |
| 591 | Morning | Jodi Prince | San Antonio | South | Salesperson | 8 | $40,600 | |
| 814 | Afternoon | Olen Prince | Detroit | Midwest | Salesperson | 15 | $71,100 | |
| 397 | Morning | Cathy Quinn | San Antonio | South | Salesperson | 19 | $48,900 | |
| 18 | Afternoon | William Ramirez | San Antonio | South | Salesperson | 13 | $28,400 | |
| 346 | Afternoon | Mickey Ramirez | Houston | South | Salesperson | 21 | $69,300 | |
| 557 | Morning | Lorie Ramirez | Philadelphia | East | Salesperson | 17 | $62,300 | |
| 954 | Morning | Letitia Ramsey | Chicago | Midwest | Salesperson | 30 | $30,400 | |
| 1071 | Morning | Simone Ramsey | San Antonio | South | Salesperson | 7 | $71,100 | |
| 205 | Afternoon | Raymond Randall | San Diego | West | Salesperson | 3 | $59,600 | |
| 548 | Afternoon | Jim Randall | New York | East | Salesperson | 7 | $52,200 | |
| 871 | Afternoon | Dusty Randall | San Antonio | South | Salesperson | 15 | $40,000 | |
| 1141 | Morning | Kathrine Randall | Phoenix | West | Salesperson | 13 | $77,600 | |
| 34 | Morning | Nanette Rasmussen | Philadelphia | East | Salesperson | 24 | $65,800 | |
| 542 | Morning | Flossie Rasmussen | Houston | South | Salesperson | 8 | $42,000 | |
| 1010 | Afternoon | Junior Ray | Chicago | Midwest | Salesperson | 10 | $29,700 | |
| 997 | Afternoon | Arturo Raymond | Houston | South | Salesperson | 21 | $75,300 | |
| 96 | Morning | Shanna Reese | Philadelphia | East | Intern | 0 | $13,000 | |
| 957 | Morning | Cleo Reese | Los Angeles | West | Salesperson | 20 | $38,000 | |
| 639 | Morning | Shauna Reeves | San Antonio | South | Salesperson | 29 | $54,200 | |
| 47 | Afternoon | Kenny Reid | Detroit | Midwest | Salesperson | 25 | $46,300 | |
| 598 | Morning | Kimberley Reid | Detroit | Midwest | Salesperson | 2 | $26,700 | |
| 1149 | Morning | Berta Reid | San Antonio | South | Salesperson | 4 | $53,500 | |
| 1185 | Afternoon | Gregorio Reilly | San Antonio | South | Salesperson | 17 | $71,500 | |
| 234 | Morning | Cheri Reynolds | Dallas | South | Salesperson | 11 | $38,000 | |
| 737 | Afternoon | Fletcher Rhodes | Chicago | Midwest | Salesperson | 13 | $53,500 | |
| 406 | Afternoon | Willie Richard | Dallas | South | Salesperson | 4 | $33,400 | |
| 192 | Morning | Sheri Richardson | Houston | South | Salesperson | 21 | $78,700 | |
| 486 | Morning | Sondra Richardson | San Diego | West | Salesperson | 2 | $46,200 | |
| 92 | Morning | Sarah Richmond | Phoenix | West | Salesperson | 12 | $84,700 | |
| 354 | Afternoon | Roland Richmond | Philadelphia | East | Salesperson | 13 | $60,800 | |
| 721 | Morning | Muriel Richmond | Dallas | South | Salesperson | 26 | $51,200 | |
| 475 | Morning | Freida Riggs | Philadelphia | East | Salesperson | 1 | $55,400 | |
| 672 | Morning | Justine Riley | Houston | South | Salesperson | 1 | $70,000 | |
| 1140 | Morning | Cindy Riley | San Antonio | South | Salesperson | 3 | $68,400 | |
| 290 | Afternoon | Noel Rios | San Diego | West | Salesperson | 1 | $51,000 | |
| 620 | Morning | Benita Rios | Phoenix | West | Salesperson | 24 | $68,900 | |
| 28 | Morning | Melanie Rivas | Houston | South | Salesperson | 9 | $84,100 | |
| 716 | Morning | Ashlee Rivas | Chicago | Midwest | Salesperson | 3 | $66,100 | |
| 681 | Morning | Coleen Rivera | Philadelphia | East | Salesperson | 18 | $53,700 | |
| 477 | Afternoon | Marvin Rivers | Dallas | South | Salesperson | 17 | $77,900 | |
| 859 | Morning | Bertie Rivers | San Antonio | South | Salesperson | 14 | $73,300 | |
| 966 | Morning | Agnes Rivers | Detroit | Midwest | Salesperson | 30 | $64,900 | |
| 678 | Afternoon | Sidney Robbins | Philadelphia | East | Salesperson | 27 | $42,700 | |
| 624 | Afternoon | Rodolfo Roberson | San Diego | West | Salesperson | 9 | $65,900 | |
| 762 | Morning | Ginger Roberson | New York | East | Salesperson | 26 | $47,500 | |
| 487 | Afternoon | Barry Roberts | Los Angeles | West | Intern | 0 | $13,100 | |
| 232 | Morning | Bernadette Robertson | Houston | South | Salesperson | 23 | $35,800 | |
| 539 | Afternoon | Ralph Robertson | Chicago | Midwest | Salesperson | 30 | $36,400 | |
| 853 | Afternoon | Hollis Robles | Detroit | Midwest | Salesperson | 27 | $39,100 | |
| 865 | Afternoon | Warren Robles | Dallas | South | Salesperson | 22 | $47,400 | |
| 77 | Afternoon | Leonard Rodriguez | San Diego | West | Salesperson | 19 | $35,900 | |
| 789 | Afternoon | Devin Rodriguez | Dallas | South | Salesperson | 17 | $68,500 | |
| 1060 | Morning | Cecilia Rogers | Detroit | Midwest | Salesperson | 15 | $43,100 | |
| 73 | Morning | Mollie Roman | Dallas | South | Salesperson | 19 | $32,500 | |
| 184 | Afternoon | Darius Roman | Dallas | South | Salesperson | 14 | $51,200 | |
| 1130 | Morning | Alexis Roman | Dallas | South | Salesperson | 15 | $30,900 | |
| 643 | Morning | Katy Romero | Phoenix | West | Salesperson | 10 | $34,200 | |
| 867 | Afternoon | Edward Romero | San Diego | West | Salesperson | 5 | $59,500 | |
| 448 | Morning | Eleanor Rosario | Phoenix | West | Salesperson | 28 | $80,200 | |
| 925 | Afternoon | Jamie Rosario | San Diego | West | Salesperson | 29 | $37,900 | |
| 165 | Afternoon | Russ Rose | San Diego | West | Salesperson | 16 | $81,700 | |
| 663 | Afternoon | Danial Rose | Phoenix | West | Salesperson | 13 | $38,600 | |
| 493 | Afternoon | Marion Roth | Houston | South | Salesperson | 24 | $29,100 | |
| 559 | Morning | Delores Rowland | Phoenix | West | Salesperson | 26 | $35,900 | |
| 970 | Morning | Tracy Rowland | Chicago | Midwest | Salesperson | 30 | $72,800 | |
| 518 | Morning | Deirdre Ruiz | San Antonio | South | Salesperson | 15 | $47,200 | |
| 573 | Morning | Shelby Ruiz | San Antonio | South | Salesperson | 11 | $45,800 | |
| 784 | Afternoon | Salvatore Ruiz | Chicago | Midwest | Salesperson | 17 | $32,700 | |
| 693 | Afternoon | Julian Rush | Detroit | Midwest | Salesperson | 23 | $36,500 | |
| 1170 | Afternoon | Gil Rush | Phoenix | West | Salesperson | 21 | $55,500 | |
| 245 | Afternoon | Colby Russell | Detroit | Midwest | Salesperson | 8 | $68,700 | |
| 307 | Afternoon | Nathan Russell | San Diego | West | Salesperson | 25 | $44,100 | |
| 740 | Morning | Vickie Russell | Chicago | Midwest | Salesperson | 30 | $64,100 | |
| 41 | Morning | Patricia Russo | Phoenix | West | Salesperson | 21 | $61,700 | |
| 698 | Morning | Jana Russo | Los Angeles | West | Salesperson | 1 | $73,700 | |
| 398 | Morning | Vicky Salas | Los Angeles | West | Salesperson | 7 | $43,000 | |
| 935 | Morning | Angelina Salas | Los Angeles | West | Salesperson | 12 | $50,200 | |
| 753 | Morning | Lauri Salazar | New York | East | Salesperson | 23 | $29,200 | |
| 151 | Morning | Megan Sampson | Phoenix | West | Salesperson | 24 | $65,200 | |
| 1190 | Morning | Aisha Sampson | San Diego | West | Salesperson | 28 | $31,600 | |
| 708 | Morning | Arline Sanders | San Antonio | South | Salesperson | 13 | $41,900 | |
| 485 | Afternoon | Gerry Sandoval | San Diego | West | Salesperson | 12 | $33,300 | |
| 1101 | Morning | Stefanie Sandoval | Houston | South | Salesperson | 18 | $47,300 | |
| 843 | Afternoon | Robby Santana | Chicago | Midwest | Salesperson | 18 | $51,900 | |
| 491 | Morning | Deborah Santiago | Houston | South | Salesperson | 5 | $54,900 | |
| 952 | Morning | Eva Santiago | Houston | South | Salesperson | 15 | $25,700 | |
| 386 | Afternoon | Hiram Santos | Detroit | Midwest | Salesperson | 24 | $37,400 | |
| 972 | Afternoon | Anthony Santos | Los Angeles | West | Salesperson | 5 | $83,900 | |
| 308 | Afternoon | Rufus Savage | Houston | South | Salesperson | 6 | $59,100 | |
| 311 | Afternoon | Julio Savage | Dallas | South | Salesperson | 21 | $64,400 | |
| 881 | Morning | Lottie Savage | San Antonio | South | Salesperson | 18 | $79,100 | |
| 563 | Afternoon | Branden Sawyer | Dallas | South | Salesperson | 7 | $34,000 | |
| 1062 | Morning | Caitlin Schaefer | Dallas | South | Salesperson | 25 | $57,500 | |
| 45 | Morning | Kathrine Schneider | Detroit | Midwest | Salesperson | 20 | $40,600 | |
| 587 | Morning | Cleo Schneider | New York | East | Salesperson | 28 | $47,100 | |
| 742 | Morning | Rosetta Schneider | San Antonio | South | Salesperson | 2 | $59,100 | |
| 281 | Afternoon | Caleb Schroeder | Houston | South | Salesperson | 21 | $84,400 | |
| 659 | Morning | Bernadette Schroeder | San Antonio | South | Salesperson | 30 | $79,200 | |
| 943 | Afternoon | Garland Scott | Houston | South | Salesperson | 27 | $62,600 | |
| 999 | Morning | Nona Scott | Dallas | South | Salesperson | 1 | $58,000 | |
| 1059 | Morning | Lorna Sellers | Detroit | Midwest | Salesperson | 12 | $27,900 | |
| 337 | Morning | Lynn Sexton | Philadelphia | East | Salesperson | 14 | $48,700 | |
| 1199 | Morning | Bridgette Sexton | San Diego | West | Salesperson | 27 | $31,000 | |
| 210 | Morning | Myrna Shaffer | Dallas | South | Salesperson | 8 | $52,900 | |
| 507 | Afternoon | Reuben Shaffer | Phoenix | West | Salesperson | 8 | $58,200 | |
| 1079 | Morning | Karen Shaffer | Philadelphia | East | Salesperson | 18 | $35,600 | |
| 137 | Afternoon | Donnie Shah | Houston | South | Salesperson | 14 | $38,700 | |
| 432 | Morning | Melissa Shah | Houston | South | Salesperson | 27 | $57,800 | |
| 1015 | Afternoon | Emanuel Shah | New York | East | Salesperson | 24 | $67,900 | |
| 1050 | Afternoon | Jimmie Shannon | Phoenix | West | Salesperson | 29 | $30,500 | |
| 703 | Morning | Kay Sharp | Dallas | South | Salesperson | 30 | $36,600 | |
| 749 | Afternoon | Kareem Sharp | Houston | South | Salesperson | 9 | $67,500 | |
| 790 | Morning | Tasha Sharp | Phoenix | West | Salesperson | 28 | $47,000 | |
| 1052 | Morning | Corine Shaw | Philadelphia | East | Salesperson | 7 | $45,100 | |
| 454 | Morning | Etta Shea | Detroit | Midwest | Salesperson | 2 | $40,000 | |
| 72 | Morning | Essie Shelton | New York | East | Salesperson | 7 | $39,900 | |
| 460 | Morning | Dona Shepard | Chicago | Midwest | Salesperson | 17 | $32,300 | |
| 868 | Morning | Sharon Shepard | Philadelphia | East | Salesperson | 9 | $29,900 | |
| 560 | Morning | Chelsea Shepherd | San Diego | West | Salesperson | 5 | $70,000 | |
| 805 | Morning | Dorothy Sherman | New York | East | Salesperson | 12 | $30,100 | |
| 167 | Morning | April Shields | San Diego | West | Salesperson | 14 | $41,200 | |
| 1009 | Afternoon | Reinaldo Shields | Phoenix | West | Salesperson | 15 | $33,900 | |
| 1013 | Morning | Leonor Short | Chicago | Midwest | Salesperson | 3 | $63,100 | |
| 1035 | Afternoon | Everett Short | San Diego | West | Salesperson | 11 | $79,800 | |
| 102 | Morning | Katelyn Silva | Los Angeles | West | Salesperson | 25 | $72,100 | |
| 723 | Afternoon | Heriberto Silva | Dallas | South | Salesperson | 16 | $80,300 | |
| 830 | Afternoon | Adam Silva | Chicago | Midwest | Salesperson | 26 | $83,900 | |
| 1038 | Afternoon | Pete Simon | Dallas | South | Salesperson | 22 | $68,300 | |
| 1191 | Afternoon | Irvin Simon | Phoenix | West | Salesperson | 26 | $65,100 | |
| 388 | Morning | Aida Sims | Phoenix | West | Salesperson | 28 | $63,700 | |
| 499 | Morning | Kathie Sims | Dallas | South | Salesperson | 6 | $57,800 | |
| 700 | Morning | Vilma Sims | New York | East | Salesperson | 1 | $30,300 | |
| 932 | Afternoon | Isiah Sims | Phoenix | West | Salesperson | 4 | $73,300 | |
| 929 | Morning | Hallie Skinner | Houston | South | Salesperson | 26 | $33,200 | |
| 937 | Afternoon | Tod Skinner | Phoenix | West | Salesperson | 2 | $31,900 | |
| 772 | Afternoon | Lance Sloan | Detroit | Midwest | Salesperson | 29 | $77,000 | |
| 930 | Afternoon | Bill Sloan | Phoenix | West | Intern | 1 | $11,900 | |
| 646 | Afternoon | Kenton Smith | Detroit | Midwest | Salesperson | 3 | $71,400 | |
| 1129 | Afternoon | Alphonse Smith | Phoenix | West | Salesperson | 7 | $61,500 | |
| 472 | Morning | Alyce Snow | Detroit | Midwest | Salesperson | 1 | $65,200 | |
| 1096 | Morning | Dolly Solis | New York | East | Salesperson | 5 | $46,500 | |
| 40 | Afternoon | Tom Solomon | Dallas | South | Salesperson | 4 | $75,400 | |
| 275 | Afternoon | Dante Solomon | Phoenix | West | Salesperson | 11 | $35,100 | |
| 589 | Morning | Amie Solomon | Chicago | Midwest | Salesperson | 12 | $27,000 | |
| 29 | Afternoon | Bert Sosa | Philadelphia | East | Salesperson | 15 | $74,200 | |
| 665 | Morning | Mona Sosa | San Antonio | South | Salesperson | 18 | $69,700 | |
| 826 | Morning | Maritza Sosa | Houston | South | Salesperson | 30 | $47,300 | |
| 621 | Morning | Sheena Spence | San Diego | West | Salesperson | 13 | $56,600 | |
| 372 | Morning | Freida Stanley | New York | East | Salesperson | 5 | $41,400 | |
| 418 | Morning | Carolina Stanton | Chicago | Midwest | Salesperson | 8 | $58,200 | |
| 357 | Afternoon | Olen Stark | Dallas | South | Salesperson | 30 | $46,400 | |
| 904 | Morning | Rochelle Stark | Phoenix | West | Salesperson | 12 | $44,800 | |
| 488 | Afternoon | Louis Stein | Detroit | Midwest | Salesperson | 3 | $31,200 | |
| 38 | Morning | Yolanda Stephens | New York | East | Salesperson | 9 | $46,000 | |
| 328 | Afternoon | Bradley Stephens | Los Angeles | West | Salesperson | 14 | $31,300 | |
| 506 | Afternoon | Rory Stephens | Houston | South | Salesperson | 2 | $78,100 | |
| 896 | Afternoon | Abraham Stephenson | Phoenix | West | Salesperson | 22 | $57,700 | |
| 163 | Afternoon | Gregg Stevenson | San Antonio | South | Salesperson | 20 | $71,500 | |
| 1186 | Afternoon | Arnold Stevenson | San Diego | West | Salesperson | 5 | $81,100 | |
| 828 | Morning | Jackie Stokes | Phoenix | West | Salesperson | 27 | $66,600 | |
| 1165 | Afternoon | Reginald Stout | New York | East | Salesperson | 24 | $62,100 | |
| 1029 | Morning | Leanne Summers | San Antonio | South | Salesperson | 26 | $59,400 | |
| 839 | Morning | Margery Sutton | New York | East | Salesperson | 3 | $41,600 | |
| 78 | Morning | Zelma Swanson | San Antonio | South | Salesperson | 7 | $33,100 | |
| 152 | Morning | Sharon Swanson | Phoenix | West | Salesperson | 27 | $30,200 | |
| 362 | Afternoon | Vito Swanson | Philadelphia | East | Salesperson | 3 | $39,800 | |
| 599 | Afternoon | Billy Swanson | Los Angeles | West | Salesperson | 21 | $42,300 | |
| 625 | Morning | Tisha Swanson | Phoenix | West | Salesperson | 23 | $69,200 | |
| 13 | Afternoon | Franklin Sweeney | Phoenix | West | Intern | 0 | $9,800 | |
| 567 | Afternoon | Weston Tapia | San Antonio | South | Salesperson | 15 | $56,000 | |
| 585 | Afternoon | Roderick Tapia | San Diego | West | Salesperson | 15 | $74,300 | |
| 294 | Afternoon | Percy Tate | Philadelphia | East | Salesperson | 3 | $81,000 | |
| 360 | Afternoon | Aaron Tate | Los Angeles | West | Salesperson | 8 | $79,200 | |
| 541 | Afternoon | Desmond Tate | New York | East | Salesperson | 14 | $67,700 | |
| 694 | Morning | Colette Tate | San Antonio | South | Salesperson | 25 | $42,100 | |
| 1022 | Afternoon | Everett Tate | Houston | South | Salesperson | 2 | $46,800 | |
| 190 | Morning | Vicky Terry | San Antonio | South | Salesperson | 26 | $71,300 | |
| 697 | Morning | Jodie Terry | New York | East | Salesperson | 21 | $51,600 | |
| 743 | Morning | Lorena Thomas | Los Angeles | West | Salesperson | 14 | $37,600 | |
| 602 | Afternoon | Avery Thompson | Phoenix | West | Salesperson | 3 | $68,400 | |
| 988 | Afternoon | Amos Thornton | Chicago | Midwest | Intern | 0 | $5,500 | |
| 994 | Morning | Sharon Thornton | Dallas | South | Salesperson | 7 | $48,000 | |
| 315 | Afternoon | Branden Todd | Philadelphia | East | Salesperson | 18 | $78,500 | |
| 764 | Afternoon | Randell Torres | Los Angeles | West | Salesperson | 27 | $65,300 | |
| 887 | Afternoon | Elliott Torres | New York | East | Salesperson | 27 | $49,800 | |
| 185 | Morning | Rosalinda Townsend | San Diego | West | Salesperson | 3 | $30,800 | |
| 4 | Morning | Jane Trevino | San Diego | West | Salesperson | 22 | $31,300 | |
| 431 | Afternoon | Avery Trevino | Phoenix | West | Salesperson | 1 | $68,300 | |
| 1 | Afternoon | Fredrick Trujillo | Phoenix | West | Salesperson | 15 | $48,100 | |
| 139 | Morning | Madge Trujillo | New York | East | Salesperson | 1 | $76,100 | |
| 552 | Afternoon | Laverne Trujillo | Philadelphia | East | Salesperson | 4 | $64,300 | |
| 329 | Afternoon | Wm Tucker | Detroit | Midwest | Intern | 1 | $10,800 | |
| 616 | Morning | Celia Tucker | Chicago | Midwest | Salesperson | 26 | $47,900 | |
| 820 | Afternoon | Bob Tucker | San Antonio | South | Salesperson | 16 | $72,400 | |
| 379 | Afternoon | Alejandro Turner | Detroit | Midwest | Salesperson | 18 | $78,800 | |
| 466 | Morning | Cecile Tyler | New York | East | Salesperson | 18 | $32,600 | |
| 169 | Morning | Gwen Underwood | Chicago | Midwest | Salesperson | 19 | $50,400 | |
| 237 | Afternoon | Garrett Underwood | Phoenix | West | Salesperson | 13 | $62,600 | |
| 271 | Afternoon | Faustino Valencia | New York | East | Salesperson | 16 | $72,500 | |
| 622 | Morning | Clarissa Valentine | Detroit | Midwest | Salesperson | 4 | $64,200 | |
| 788 | Afternoon | Omar Valenzuela | Detroit | Midwest | Salesperson | 19 | $80,400 | |
| 113 | Afternoon | Gerry Vance | Los Angeles | West | Salesperson | 26 | $64,400 | |
| 268 | Morning | Molly Vaughn | Dallas | South | Salesperson | 25 | $26,600 | |
| 815 | Morning | Sheree Vega | Phoenix | West | Salesperson | 24 | $72,400 | |
| 630 | Morning | Karen Velasquez | San Antonio | South | Salesperson | 9 | $82,700 | |
| 953 | Afternoon | Shane Velasquez | Los Angeles | West | Salesperson | 26 | $69,100 | |
| 1139 | Afternoon | Rupert Velasquez | New York | East | Salesperson | 11 | $37,100 | |
| 902 | Afternoon | Rosario Velazquez | Philadelphia | East | Salesperson | 7 | $48,700 | |
| 1073 | Morning | Isabella Velazquez | Detroit | Midwest | Intern | 0 | $10,600 | |
| 355 | Morning | Linda Velez | Houston | South | Salesperson | 8 | $41,300 | |
| 510 | Afternoon | Rudy Velez | Houston | South | Salesperson | 1 | $75,500 | |
| 993 | Morning | April Villa | San Diego | West | Salesperson | 30 | $66,500 | |
| 779 | Afternoon | Gino Villanueva | Phoenix | West | Salesperson | 30 | $58,000 | |
| 1116 | Morning | Rebekah Villanueva | Houston | South | Salesperson | 26 | $25,900 | |
| 1156 | Afternoon | Douglas Villanueva | Chicago | Midwest | Salesperson | 1 | $77,700 | |
| 31 | Afternoon | Courtney Villarreal | Chicago | Midwest | Intern | 1 | $8,000 | |
| 35 | Morning | Ursula Villarreal | Chicago | Midwest | Salesperson | 15 | $74,000 | |
| 617 | Morning | Jordan Villarreal | San Antonio | South | Salesperson | 3 | $43,000 | |
| 1105 | Morning | Ashlee Villegas | Detroit | Midwest | Salesperson | 21 | $54,300 | |
| 1138 | Afternoon | Melvin Villegas | Chicago | Midwest | Salesperson | 7 | $60,700 | |
| 490 | Afternoon | Miles Wagner | New York | East | Salesperson | 12 | $81,200 | |
| 823 | Morning | Melody Wagner | Philadelphia | East | Salesperson | 7 | $60,600 | |
| 1087 | Afternoon | Fritz Walker | Phoenix | West | Salesperson | 25 | $40,200 | |
| 463 | Afternoon | Terence Wallace | Detroit | Midwest | Salesperson | 17 | $28,500 | |
| 895 | Morning | Dianna Wallace | San Diego | West | Salesperson | 22 | $57,600 | |
| 1072 | Morning | Patti Walls | Los Angeles | West | Salesperson | 19 | $72,100 | |
| 1014 | Morning | Karina Walter | Dallas | South | Salesperson | 7 | $28,500 | |
| 117 | Morning | Leigh Ward | Phoenix | West | Salesperson | 23 | $55,300 | |
| 136 | Afternoon | Micheal Ward | Philadelphia | East | Salesperson | 19 | $47,900 | |
| 537 | Afternoon | Reynaldo Ward | Dallas | South | Salesperson | 6 | $29,500 | |
| 87 | Morning | Adeline Ware | Dallas | South | Salesperson | 8 | $76,700 | |
| 1053 | Morning | Karen Ware | Dallas | South | Salesperson | 10 | $28,100 | |
| 1180 | Morning | Ava Ware | San Diego | West | Salesperson | 9 | $50,500 | |
| 934 | Afternoon | Russell Warren | New York | East | Salesperson | 5 | $29,300 | |
| 540 | Morning | Winifred Watkins | Dallas | South | Salesperson | 7 | $60,000 | |
| 1039 | Afternoon | Robin Watkins | Chicago | Midwest | Salesperson | 29 | $43,200 | |
| 1159 | Afternoon | Lavern Watson | San Antonio | South | Salesperson | 11 | $77,200 | |
| 1131 | Morning | Delores Watts | Phoenix | West | Salesperson | 11 | $75,900 | |
| 126 | Morning | Brandie Weaver | San Antonio | South | Salesperson | 29 | $75,100 | |
| 688 | Morning | Benita Weaver | New York | East | Salesperson | 20 | $45,800 | |
| 748 | Afternoon | Kory Webb | Detroit | Midwest | Salesperson | 16 | $36,500 | |
| 973 | Afternoon | Winston Webster | New York | East | Salesperson | 19 | $31,700 | |
| 141 | Morning | Ethel Weiss | Los Angeles | West | Salesperson | 6 | $28,500 | |
| 799 | Morning | Gretchen Welch | New York | East | Intern | 1 | $6,000 | |
| 270 | Afternoon | Joel Wells | Phoenix | West | Salesperson | 9 | $32,500 | |
| 361 | Morning | Frances Wells | San Diego | West | Salesperson | 13 | $76,100 | |
| 1089 | Morning | Irene Wells | Dallas | South | Intern | 1 | $12,200 | |
| 291 | Morning | Lorie Werner | Phoenix | West | Salesperson | 4 | $42,800 | |
| 710 | Morning | Becky Wheeler | San Antonio | South | Salesperson | 21 | $53,700 | |
| 1041 | Morning | Billie Wheeler | New York | East | Salesperson | 3 | $81,400 | |
| 60 | Morning | Josefina Whitaker | San Diego | West | Salesperson | 20 | $43,200 | |
| 608 | Afternoon | Cory White | Dallas | South | Salesperson | 7 | $56,500 | |
| 319 | Afternoon | Harley Whitehead | Chicago | Midwest | Salesperson | 24 | $54,200 | |
| 872 | Morning | Lena Whitney | Chicago | Midwest | Salesperson | 3 | $42,000 | |
| 1018 | Morning | Ladonna Whitney | Chicago | Midwest | Salesperson | 13 | $65,900 | |
| 978 | Afternoon | Monty Wiggins | Chicago | Midwest | Salesperson | 23 | $28,800 | |
| 1108 | Morning | Jeri Wilcox | Dallas | South | Salesperson | 15 | $81,500 | |
| 761 | Afternoon | Jeff Wiley | Phoenix | West | Salesperson | 28 | $76,000 | |
| 607 | Afternoon | Horacio Wilkinson | San Diego | West | Salesperson | 22 | $53,600 | |
| 147 | Morning | Nicole Williams | Houston | South | Sales Manager | 24 | $87,700 | |
| 884 | Afternoon | Kirby Williams | Chicago | Midwest | Salesperson | 8 | $27,000 | |
| 522 | Morning | Reba Willis | Detroit | Midwest | Salesperson | 27 | $58,700 | |
| 455 | Afternoon | Marcel Winters | Houston | South | Salesperson | 1 | $48,600 | |
| 471 | Afternoon | Eddy Winters | San Antonio | South | Salesperson | 6 | $25,000 | |
| 1166 | Afternoon | Harrison Wise | San Antonio | South | Salesperson | 3 | $26,700 | |
| 919 | Afternoon | Clinton Wolf | Chicago | Midwest | Salesperson | 1 | $78,300 | |
| 482 | Morning | Victoria Wolfe | Houston | South | Salesperson | 27 | $55,800 | |
| 911 | Morning | Priscilla Wolfe | San Diego | West | Salesperson | 5 | $71,200 | |
| 849 | Afternoon | Timmy Wong | San Diego | West | Salesperson | 5 | $32,600 | |
| 212 | Morning | Jolene Woodard | Houston | South | Salesperson | 9 | $60,600 | |
| 1143 | Afternoon | Elton Woodard | Detroit | Midwest | Salesperson | 16 | $66,000 | |
| 1178 | Afternoon | Kirk Woodard | Chicago | Midwest | Salesperson | 19 | $69,400 | |
| 162 | Morning | Sherri Woodward | Detroit | Midwest | Salesperson | 22 | $46,700 | |
| 1181 | Afternoon | Kirk Woodward | Chicago | Midwest | Salesperson | 2 | $70,000 | |
| 509 | Morning | Candice Wright | Philadelphia | East | Salesperson | 8 | $53,100 | |
| 706 | Morning | Dina Wu | New York | East | Intern | 1 | $7,200 | |
| 189 | Morning | Patti Yang | Phoenix | West | Salesperson | 2 | $42,700 | |
| 425 | Afternoon | Anthony Yoder | Phoenix | West | Salesperson | 23 | $29,300 | |
| 824 | Afternoon | Jayson York | New York | East | Salesperson | 16 | $76,800 | |
| 43 | Morning | Nora Young | Detroit | Midwest | Salesperson | 1 | $61,200 | |
| 53 | Afternoon | Trent Young | Detroit | Midwest | Salesperson | 4 | $81,100 | |
| 173 | Morning | Robin Yu | New York | East | Salesperson | 1 | $56,900 | |
| 262 | Morning | Josefa Zavala | Detroit | Midwest | Salesperson | 19 | $51,900 | |
| 338 | Morning | Edna Zavala | Houston | South | Salesperson | 18 | $81,600 | |
| 93 | Afternoon | Robby Zhang | San Diego | West | Salesperson | 4 | $59,100 | |
| 508 | Morning | Christian Zhang | Los Angeles | West | Salesperson | 9 | $50,400 | |
| 948 | Afternoon | Art Zimmerman | Chicago | Midwest | Salesperson | 7 | $76,600 | |
| 546 | Afternoon | Raymond Zuniga | Dallas | South | Salesperson | 4 | $46,200 |
# Employees
Experience
Max Salary
Average Salary
Position
Managers
Salesperson
Project 10 instructions.docx
1.Basic Queries Assessment
This dataset has information about season 20 of The Simpsons. Write SQL queries to satisfy the following information requests.
1.1
Make an alphabetical list of episode titles.
a. Show episode title and air date.
1.2
Make a list of episodes based on their ratings.
a. Show episode title and rating.
b. Sort the list by rating in descending order, then by title in ascending order.
1.3
List the different characters in the Character_Award table.
a. Show an alphabetical list of character names.
b. Be sure your result does not contain any duplicates
1.4
Show the first 20 names of an alphabetical list of people.
a. Show the name column only
1.5
Show an abbreviated list of nicknames and the people they belong to.
a. Show the nickname and the name.
b. Sort the list by nickname.
c. Show only the first 10 records.
1.6
What are the different categories for which people are given credit for their roles in creating episodes?
a. Show an alphabetical list of categories.
b. Be sure to remove any duplicates from the query result.
1.7
List the different awards shown in the Award table.
a. Make an alphabetical list of award names.
b. Be sure each award name appears only once.
1.8
Which combination of episode and stars got the most votes?
a. Show the episode ID and the number of stars, and the number of votes.
b. Your query should produce a single record.
1.9
Which episode got the most ten-star ratings?
a. Show the episode ID and the number of votes.
b. This information is found in the Vote table.
c. Your query should produce a single record.
1.10
What is the most recent year for an award nomination and what was the award?
a. Each record in the Award table indicates an award nomination, with the outcome of the nomination shown in the result column. You do not need to refer to the result column for this query.
b. Show the organization, year, award category, and award.
c. Your query should produce a single record.
Project 10.xlsx
SQL
| Useful Links | ||
| Open data.world query editor | ||
| Show database diagram for these queries | ||
| Query 1 | ||
| To enter a data.world query, select C10 and paste it into the formula bar. | ||
| Query 2 | ||
| To enter a data.world query, select C15 and paste it into the formula bar. | ||
| Query 3 | ||
| To enter a data.world query, select C20 and paste it into the formula bar. | ||
| Query 4 | ||
| To enter a data.world query, select C25 and paste it into the formula bar. | ||
| Query 5 | ||
| To enter a data.world query, select C30 and paste it into the formula bar. | ||
| Query 6 | ||
| To enter a data.world query, select C35 and paste it into the formula bar. | ||
| Query 7 | ||
| To enter a data.world query, select C40 and paste it into the formula bar. | ||
| Query 8 | ||
| To enter a data.world query, select C45 and paste it into the formula bar. | ||
| Query 9 | ||
| To enter a data.world query, select C50 and paste it into the formula bar. | ||
| Query 10 | ||
| To enter a data.world query, select C55 and paste it into the formula bar. | ||
Project 11 instructions.docx
1.Queries with Restrictions Assessment
This database contains data about crimes reported in the city of Chicago during 2018.
1.1
What are the details of the community area named "O'Hare"?
a. Show the community area number, side, and population.
b. Remember that to refer to column value in the WHERE clause that includes an apostrophe (') you must either use double quotes around it ("O'Hare") or put a backslash in front of the apostrophe ('O\'Hare').
1.2
What is the contact information for district #22?
a. Show the district name, fax, and email.
1.3
List the crimes reported to have occurred on blocks ending in "W 93RD ST".
a. Show the date, block, report number, case number, and beat.
b. Sort the results by date then by block then by report number.
1.4
In Chicago, crimes are classified using Illinois Uniform Crime Reporting (IUCR) standards. List the IUCR secondary descriptions with a primary description of "LIQUOR LAW VIOLATION".
a. Show an alphabetical list of secondary descriptions.
1.5
List the crimes reported with an IUCR NUMBER of "520" that occurred in ward NUMBER 26 where no arrest has been made.
a. Show the date, case number, ward number, and district number.
b. Sort the results by case number then by date.
1.6
List the crimes reported in district #3 where no location description has been reported.
a. Show the date, case number, block, and ward number.
b. Sort the results by date then by case number.
1.7
List the crimes reported in district #11 with an IUCR number of 1305 along with those reported in ward #9 with an FBI code number of 13.
a. Show the case number, FBI code number, ward number, IUCR number, district number, arrest, and community area number.
b. Sort the results by IUCR number then by district number then by FBI code number then by ward number then by case number.
1.8
List the crimes reported between latitudes 41.843 and 41.844 (inclusive) that have a location description of "SMALL RETAIL STORE".
a. Show the date, case number, community area number, and district number.
b. Sort the results by date then by case number.
1.9
Which wards had at least one crime with an IUCR number of "141A" (WEAPONS VIOLATION) reported at a location with a description of "RESIDENCE"?
a. Show only the ward number in ascending order.
b. Be sure your result does not include any duplicates.
1.10
List the location descriptions where crimes with an IUCR number of "1152" (DECEPTIVE PRACTICE) have occurred in ward #12.
a. Show only the location_description in alphabetical order.
b. Be sure your result does not include any duplicates.
Project 11.xlsx
SQL
| Useful Links | ||
| Open data.world query editor | ||
| Show database diagram for these queries | ||
| Query 1 | ||
| To enter a data.world query, select C10 and paste it into the formula bar. | ||
| Query 2 | ||
| To enter a data.world query, select C15 and paste it into the formula bar. | ||
| Query 3 | ||
| To enter a data.world query, select C20 and paste it into the formula bar. | ||
| Query 4 | ||
| To enter a data.world query, select C25 and paste it into the formula bar. | ||
| Query 5 | ||
| To enter a data.world query, select C30 and paste it into the formula bar. | ||
| Query 6 | ||
| To enter a data.world query, select C35 and paste it into the formula bar. | ||
| Query 7 | ||
| To enter a data.world query, select C40 and paste it into the formula bar. | ||
| Query 8 | ||
| To enter a data.world query, select C45 and paste it into the formula bar. | ||
| Query 9 | ||
| To enter a data.world query, select C50 and paste it into the formula bar. | ||
| Query 10 | ||
| To enter a data.world query, select C55 and paste it into the formula bar. | ||
Project 12 instructions.docx
1.Join Queries Assessment
This database contains data about crimes reported in the city of Chicago during 2018.
1.1
Make a community-area neighborhood list.
a. Show the side, community area name, and the name of the neighborhood.
b. Sort the results by side then by community area name then by neighborhood
1.2
List the crimes reported to have occurred in vehicles in ward 8 with a IUCR primary description of "BATTERY".
a. Show the secondary description, district, block, and location description.
b. Crimes occurring in vehicles have a location that starts with "vehicle".
c. Sort the results by district then by secondary description.
1.3
List the crimes reported with a primary description of "PUBLIC PEACE VIOLATION" that occurred in ward #5 where an arrest has been made.
a. Show the primary description, ward, district, date, and community area number.
b. Sort the results by district then by ward then by primary description.
1.4
List the crimes reported between latitudes 42.013 and 42.014 (inclusive) that have a location description of "STREET".
a. Show the title from the FBI_code table, ward, district, arrest, and FBI code.
b. Sort the results by ward then by district then by title.
1.5
Build a detailed list of all crimes reported to have occurred in locations under management of the Chicago Transit Authority in ward number 32 where no arrest has been made.
· Show the case number, IUCR primary description, IUCR secondary description, block, and district number.
· Order the result by primary description then secondary description.
· Note: locations under the management of the Chicago Transit Authority begin with "CTA".
1.6
Build a detailed list of crimes against Society reported to have occurred in district 1 at locations with a description of "BANK".
· Show the case number, IUCR primary description, IUCR secondary description, date, and beat.
· Order the result by secondary description then primary description.
· Note: Only include crimes where the "crime_against" column equals "Society".
1.7
Build a community-area crime report showing each incident of liquor law violation.
a. Include crimes reported in Chicago's "Far North" side with a primary description of "LIQUOR LAW VIOLATION".
b. Show the community area name, secondary description, arrest, date, and block columns.
c. Sort the results by arrest then by community area name then by secondary description.
1.8
List the crimes reported to have occurred at exactly 8:00 am on January 12, 2018.
a. Show the title from the FBI_code table, ward, district, arrest, and community area number.
b. Sort the results by title then by district then by ward.
1.9
Suppose you work for alderman Pat Dowell in the office of ward #3. The alderman would like to discuss progress on the cases of a particular category of crime with commanders of the districts where the crimes occurred. Generate a list to facilitate this discussion.
a. List all crimes reported in ward #3 with an IUCR primary description of "CRIM SEXUAL ASSAULT".
b. Show the primary_description, secondary_description, district, arrest, case_number, commander, district phone number, FBI code, and beat.
c. Sort the results by district then by arrest then by secondary description then by case_number.
1.10
Suppose you live in the Smith Park neighborhood and are concerned about the rise in a particular category of crime. You have a meeting scheduled with a member of your alderman's staff to discuss the issue. To prepare for the meeting, you need to prepare a list of the crimes.
a. List all crimes reported in the community area associated with your neighborhood (Smith Park) with an IUCR primary description of "PUBLIC PEACE VIOLATION".
b. Show the primary_description, secondary_description, date, beat, and location description.
c. Sort the results by arrest then by secondary description.
Project 12.xlsx
Sheet1
| Useful Links | ||
| Open data.world query editor | ||
| Show database diagram for these queries | ||
| Query 1 | ||
| To enter a data.world query, select C10 and paste it into the formula bar. | ||
| Query 2 | ||
| To enter a data.world query, select C15 and paste it into the formula bar. | ||
| Query 3 | ||
| To enter a data.world query, select C20 and paste it into the formula bar. | ||
| Query 4 | ||
| To enter a data.world query, select C25 and paste it into the formula bar. | ||
| Query 5 | ||
| To enter a data.world query, select C30 and paste it into the formula bar. | ||
| Query 6 | ||
| To enter a data.world query, select C35 and paste it into the formula bar. | ||
| Query 7 | ||
| To enter a data.world query, select C40 and paste it into the formula bar. | ||
| Query 8 | ||
| To enter a data.world query, select C45 and paste it into the formula bar. | ||
| Query 9 | ||
| To enter a data.world query, select C50 and paste it into the formula bar. | ||
| Query 10 | ||
| To enter a data.world query, select C55 and paste it into the formula bar. | ||
