Need Statistics Assignment Urgently - 4 Hours
Stat HW #1/Proteintxt.csv
Food,Calories,Protein,%Calories fat,%Calories saturated fat,Cholesterol "Beef, ground, extra lean",250,25,58,23,82 "Beef, ground, regular",287,23,66,26,87 "Beef, round",184,28,24,12,82 Brisket,263,28,54,21,91 Flank steak,244,28,51,22,71 Lamb leg roast,191,28,38,16,89 "Lamb loin chop, broiled",215,30,42,17,94 "Liver, fried",217,27,36,12,482 Pork loin roast,240,27,52,18,90 Sirloin,208,30,37,15,89 Spareribs,397,29,67,27,121 "Veal cutlet, fried",183,33,42,20,127 Veal rib roast,175,26,37,15,131 "Chicken, with skin, roasted",239,27,51,14,88 "Chicken, no skin, roast",190,29,37,10,89 "Turkey, light meat, no skin",157,30,18,6,69 Clams,98,16,6,0,39 Cod,98,22,8,1,74 Flounder,99,21,12,2,54 Mackerel,199,27,77,20,100 Ocean perch,110,23,13,3,53 Salmon,182,27,24,5,93 Scallops,112,23,8,1,56 Shrimp,116,24,15,2,156 Tuna,181,32,41,10,48
__MACOSX/Stat HW #1/._Proteintxt.csv
Stat HW #1/HotelAwaytxt.csv
Nationality,Price (US$) United States,174 China,161 United Kingdom,167 Brazil,171 New Zealand,163 France,141 Canada,147 India,154
__MACOSX/Stat HW #1/._HotelAwaytxt.csv
Stat HW #1/CigaretteTaxtxt.csv
State,Cigarette Tax Alabama,0.425 Alaska,2 Arizona,2 Arkansas,1.15 California,0.87 Colorado,0.84 Connecticut,3.4 Delaware,1.6 Florida,1.339 Georgia,0.37 Hawaii,3.2 Idaho,0.57 Illinois,1.98 Indiana,0.995 Iowa,1.36 Kansas,0.79 Kentucky,0.6 Louisiana,0.36 Maine,2 Maryland,2 Massachusetts,3.51 Michigan,2 Minnesota,2.9 Mississippi,0.68 Missouri,0.17 Montana,1.7 Nebraska,0.64 Nevada,0.8 New Hampshire,1.78 New Jersey,2.7 New Mexico,1.66 New York,4.35 North Carolina,0.45 North Dakota,0.44 Ohio,1.25 Oklahoma,1.03 Oregon,1.31 Pennsylvania,1.6 Rhode Island,3.5 South Carolina,0.57 South Dakota,1.53 Tennessee,0.62 Texas,1.41 Utah,1.7 Vermont,2.75 Virginia,0.3 Washington,3.025 West Virginia,0.55 Wisconsin,2.52 Wyoming,0.6
__MACOSX/Stat HW #1/._CigaretteTaxtxt.csv
Stat HW #1/.DS_Store
__MACOSX/Stat HW #1/._.DS_Store
Stat HW #1/DomesticBeertxt.csv
Brand,Alcohol %,Carbohydrates,Calories American Amber Lager,4.1,10.5,136 American Lager,4.1,10.5,132 American Light,3.2,7.6,96 Anchor Steam,4.9,16,153 Anheuser Busch Natural Ice,5.9,8.9,157 Anheuser Busch Natural Light,4.2,3.2,95 Aspen Edge,4.1,2.6,94 Bards Gold (Gluten Free),4.6,14.2,155 Big Sky Moose Drool Brown Ale,5.2,15.6,177 Big Sky Scape Goat Pale Ale,4.7,13.9,163 Big Sky Summer Honey Ale,4.7,11.6,154 Big Sky Trout Slayer Ale,4.7,13.9,163 Blatz Beer,4.8,12.5,153 Blue Moon,5.4,13.7,171 Breckenbridge Brown Ale,5.6,21.9,196 Breckenbridge Red Ale,5.5,21.8,193 Bud Dry,5,7.8,130 Bud Ice,5.5,8.9,123 Bud Ice Light,5,7.5,115 Bud Light,4.2,6.6,110 Bud Light Lime,4.2,8,116 Bud Light Platimum,6,4.4,137 Budweiser,5,10.6,145 Budweiser Black Crown,6,10,146 Budweiser Select,4.3,3.1,99 Budweiser Select 55,2.4,1.9,55 Busch Beer,4.6,10.2,133 Busch Ice,5.9,12.5,169 Busch Light,4.1,3.2,95 Carling Black Label,4.3,12.5,138 Colt 45 Malt Liquor,6.1,11.1,174 Coors,4.9,12.2,149 Coors Light,4.2,5.3,104 Deschutes Twilight Summer Ale,5,9.6,162 Deschutes Chainbreaker White IPA,5.6,10.8,180 Extra Gold Lager,5,12.5,152 Flying Dog Dog Schwarz,7.8,14.3,222 Flying Dog Doggie Style,4.7,11.4,158 Flying Dog Dogtober Fest,5.6,12.3,165 Flying Dog Double Dog,11.5,16.6,313 Flying Dog Garde Dog,5.5,9.3,150 Flying Dog Gonzo,9.2,18.9,271 Flying Dog Horn Dog,10.2,21.5,314 Flying Dog In Heat Wheat,4.7,10.4,138 Flying Dog Kerberos Tripel,8.3,8.6,238 Flying Dog K-9 Cruiser,7.4,10.6,197 Flying Dog Old Scratch Amber Ale,5.5,10.2,154 Flying Dog Raging Bitch,8.3,11.6,221 Flying Dog Road Dog,6,9.9,163 Flying Dog Snake Dog IPA,7.1,10,188 Flying Dog Tire Bite Golden Ale,5.1,6.2,129 Flting Dog Wild Dog: Coffee Stout,9.9,20.2,288 Flying Dog Woody Creek White,4.8,8,131 Genesee Beer,4.5,13.5,148 Genesee Cream Ale,5.1,15,162 Genesee Ice,5.9,14.5,156 Genesee Red,4.9,14,148 George Killian's Irish Red,5,14.8,162 Hamm's Beer,4.7,12.1,144 Hamm's Golden Draft,4.7,12.1,144 Hamm's Special Light,3.9,8.3,110 Icehouse,5,8.7,132 Icehouse 5.0,5.5,9.8,149 Icehouse Light,4.1,5,103 Keystone Ice,5.9,5.9,142 Keystone Light,4.1,5,103 Keystone Premium,4.4,5.8,111 Leinenkugel Amber Light,4.1,7.4,110 Leinenkugel Creamy Dark,4.9,16.8,170 Leinenkugel Honey Weiss,4.9,12,149 Leinenkugel Light,4.2,5.7,105 Leinenkugel Northwoods Lager,4.9,15.3,163 Leinenkugel Original,4.7,13.9,152 Leinenkugel Red,4.9,16.2,166 Leinenkugel Sunset Wheat,4.9,16,165 Magnum Malt Liquor,5.6,11.2,157 Michael Shea's,4.6,13,145 Michelob Amber Boch,5.2,15,166 Michelob Beer,5,13.3,155 Michelob Golden Draft,4.7,14.1,152 Michelob Golden Draft Light,4.1,7,110 Michelob Honey Lager,4.9,17.9,175 Michelob Light,4.3,6.7,113 Michelob Ultra,4.1,2.6,95 Mickey's Fine Malt Liquor,5.6,11.2,157 Mickey's Ice,5.8,11.8,157 Miller Chill,4.2,6.5,110 Miller Genuine Draft,4.7,13.1,143 Miller Genuine Draft Light,4.2,7,110 Miller Genuine Draft 64,2.8,2.4,64 Miller High Life,4.7,13.1,143 Miller High Life Light,4.2,7,110 Miller Lite,4.2,3.2,96 Miller Lite Brwer's Collection Amber,4.2,6.2,110 Miller Lite Brwer's Collection Blonde,4.2,6.2,110 Miller Lite Brwer's Collection Wheat,4.2,6.2,110 Milwaukee's Best,4.3,11.4,128 Milwaukee's Best Ice,5.9,7.3,144 Milwaukee's Best Light,4.2,3.5,98 Milwaukee's Best Premium,4.2,11.4,128 New Belgium 1554,5.6,25,205 New Belgium 2 Below,6.6,17,200 New Belgium Abbey,7,18,200 New Belgium Blue Paddle,4.8,14,140 New Belgium Fat Tire,5.2,15,160 New Belgium Mothership Wit,4.8,15,155 New Belgium Sunshine Wheat,4.8,13,145 New Belgium Trippel,7.8,20,215 Old Milwaukee Beer,4.5,12.9,146 Old Milwaukee Light,3.8,8.3,114 Olde English 800,5.9,10.5,160 Olde English 800 7.5,7.5,13.4,202 Olde English High Gravity 800,8,14.6,220 Olympia Premium Lager,4.7,11.9,146 Pabst Blue Ribbon,4.7,12,144 Pete's Wicked Ale,5.3,17.7,174 Red Dog,5,14.1,147 Red Hook ESB,5.8,14.2,179 Red Hook IPA,6.5,12.7,188 Rolling Rock Premium Beer,4.5,10,120 Sam Adams Boston Ale,4.9,19.9,160 Sam Adams Boston Lager,4.8,18,160 Sam Adams Cherry Wheat,5.2,16.9,166 Sam Adams Cream Stout,4.7,23.9,195 Sam Adams Light,4.1,9.6,119 Schaefer,4.6,12,142 Schlitz,4.7,12.1,146 Sierra Nevada Anniversary Ale,5.9,17.3,190 Sierra Nevada Bigfoot,9.6,32.1,330 Sierra Nevada Celebration Ale,6.8,19.4,214 Sierra Nevada Draft Ale,5,13.4,157 Sierra Nevada Early Spring Beer,5.9,16.7,190 Sierra Nevada Harvest Ale,6.7,19.3,215 Sierra Nevada India Pale Ale,6.9,20,231 Sierra Nevada Pale Ale,5.6,14.1,175 Sierra Nevada Pale Bock,7,19.7,218 Sierra Nevada Porter,5.6,18.4,194 Sierra Nevada Stout,5.8,22.3,225 Sierra Nevada Summerfest Beer,5,13.7,158 Sierra Nevada Wheat Beer,4.4,13.1,153 Southpaw Light,5,6.6,123 Steel Reserve,8.1,16,222 Steel Reserve Six,6,11,160 Steel Reserve Triple Export,8.1,16,222 Stroh's Beer,4.6,12,149 Stroh's Light,4.4,7,113 Summit Horizon Red IPA,5.7,15,195 Summit Summer Ale,4.5,12,147 Weinhard's Amber Light,4.2,11.5,135 Weinhard's Blonde Lager,5.1,14,161 Weinhard's Hefweizen,4.9,12.2,151 Weinhard's Pale Ale,4.6,13,147 Weinhard's Private Reserve,4.8,9.9,150 Yuengling Ale,5,10,145 Yuengling Lager,4.4,12,135 Yuengling Light,3.8,6.6,98 Yuengling Porter,4.5,14,150 Yuengling Premium Beer,4.4,12,135
__MACOSX/Stat HW #1/._DomesticBeertxt.csv
Stat HW #1/PropertyTaxestxt.csv
State,Property Taxes Per Capita ($) Alabama,506 Alaska,1714 Arizona,1071 Arkansas,548 California,1458 Colorado,1253 Connecticut,2498 Delaware,714 D.C.,2985 Florida,1593 Georgia,1062 Hawaii,1016 Idaho,812 Illinois,1763 Indiana,1127 Iowa,1312 Kansas,1354 Kentucky,662 Louisiana,698 Maine,1655 Maryland,1206 Massachusetts,1845 Michigan,1445 Minnesota,1345 Mississippi,794 Missouri,922 Montana,1308 Nebraska,1443 Nevada,1331 New Hampshire,2424 New Jersey,2671 New Mexico,611 New York,2105 North Carolina,867 North Dakota,1191 Ohio,1133 Oklahoma,598 Oregon,1161 Pennsylvania,1230 Rhode Island,2020 South Carolina,970 South Dakota,1098 Tennessee,746 Texas,1461 Utah,834 Vermont,2065 Virginia,1430 Washington,1217 West Virginia,718 Wisconsin,1633 Wyoming,2321
__MACOSX/Stat HW #1/._PropertyTaxestxt.csv
Stat HW #1/NBACosttxt.csv
Team Name,Cost ($) Atlanta Hawks,245.39 Boston Celtics,444.16 Brooklyn Nets,404.6 Charlotte Bobcats,212.4 Chicago Bulls,477.32 Cleveland Cavaliers,271.74 Dallas Mavericks,312.2 Denver Nuggets,322.5 Detroit Pistons,261.2 Golden State Warriors,336.52 Houston Rockets,369.86 Indiana Pacers,232.44 Los Angeles Clippers,435.72 Los Angeles Lakers,541 Memphis Grizzlies,223.92 Miami Heat,468.2 Milwaukee Bucks,325.85 Minnesota Timberwolves,281.06 New Orleans Pelicans,221.8 New York Knicks,676.42 Oklahoma City Thunder,295.4 Orlando Magic,263.1 Philadelphia 76ers,278.9 Phoenix Suns,341.9 Portland Trail Blazers,317.08 Sacramento Kings,280.28 San Antonio Spurs,340.6 Toronto Raptors,289.71 Utah Jazz,275.74 Washington Wizards,258.78
__MACOSX/Stat HW #1/._NBACosttxt.csv
Stat HW #1/Utilitytxt.csv
Utility Charge 96 171 202 178 147 102 153 197 127 82 157 185 90 116 172 111 148 213 130 165 141 149 206 175 123 128 144 168 109 167 95 163 150 154 130 143 187 166 139 149 108 119 183 151 114 135 191 137 129 158
__MACOSX/Stat HW #1/._Utilitytxt.csv
Stat HW #1/Barrierstxt.csv
Barriers,Frequency Data must be integrated from multiple sources,68 Lack of automation/repeatable process,51 Metrics need to be identified or defined,45 Production is cumbersome,42 Data quality is not reliable,36 Sharing findings is challenging,21 Analytic tools are too complex,17 Ensuring security and integrity of workforce data,17 Other,3
__MACOSX/Stat HW #1/._Barrierstxt.csv
Stat HW #1/Problem Sets 1.docx
Problem Set #1: ECON 216
Professor Derrick Robinson
All datasets used in the problem set can be located at: Blackboard Problems Set 1 post
1.1) For each of the following variables, determine whether the variable is categorical or numerical and its scale of measurement. If variable is numerical: discrete or continuous?
|
|
Categorical (Y=1/N=0) |
Numerical (Y=1/N=0) |
Scale/Measure |
Discrete (Y=1/N=0) |
Continuous (Y=1/N=0) |
|
Household Cellphone Count |
|
|
|
|
|
|
Monthly Data Usage (Megabytes) |
|
|
|
|
|
|
Text Message Count |
|
|
|
|
|
|
Time Spent Shopping in Bookstore |
|
|
|
|
|
|
Number of Purchased Textbooks |
|
|
|
|
|
|
Academic Major |
|
|
|
|
|
|
Gender |
|
|
|
|
|
|
Amount of Money Spent on Clothing |
|
|
|
|
|
|
Favorite Department Store |
|
|
|
|
|
|
Number of Shoes Owned |
|
|
|
|
|
|
Monthly Mortgage Payments |
|
|
|
|
|
|
Number of Jobs Worked over 20 years |
|
|
|
|
|
|
Annual Household Income |
|
|
|
|
|
|
Marital Status |
|
|
|
|
|
1.11) The director of market research at a Trader Joes wanted to conduct a survey in San Diego to understand how much time working women spend shopping for clothing in a typical month.
a) What type of data will the director need to collect?
b) Develop three categorical questions for a potential survey meant to collect this information.
c) Develop three numerical questions that for a potential survey meant to collect this information.
1.26) Clean the following list of data, which indicates cellphone brands owned by a sample of 20 respondents.
a) Place data in a table and identify any irregularities. Clean the data by resolving irregularities.
b) Identify any missing values.
Apple, Samsung, Appel, Nokia, Blackberry, HTC, Apple, Samsung, HTC, LG, Blueberry, Samsung, Samsong, APPLE, Motorola, Apple, Samsun, Appl, Samsung.
1.46) The Pew Research Center releases reports based on surveys at its website, www.pewresearch.org. Visit this site and read an article of interest.
a) Provide a link to the article
b) Describe the population of interest
c) Describe the sample that was collected
d) Describe a parameter of interest
e) Describe the statistic used to estimate the parameter in (d)
1.48) The American Community Survey (www.census.gov/acs ) provides data every year about communities in the United States. Addresses are randomly selected, and respondents are required to supply answers to a series of questions.
a) Describe a variable for which data is collected.
b) Is the variable categorical or numerical?
c) If the variable is numerical, is it discrete or continuous?
2.6) The following table represents world oil production in millions of barrels a day in 2013:
|
Region |
Oil Production (mil of barrels a day) |
|
Iran |
2.69 |
|
Saudi Arabia |
9.58 |
|
Other OPEC countries |
17.93 |
|
Non-OPEC countries |
51.99 |
a) Compute the percentage of values in each category.
b) What conclusions can you reach concerning the production of oil in 2013?
2.7 & 2.29) Visier’s 2014 Survey of Employers explores current workforce analytics and planning practices, investments, and future plans. U.S.-based employers were asked what they see as the most common technical barrier to workforce analytics. The responses, stored in Barriers dataset, were as follows:
|
Barriers |
Frequency |
|
Data must be integrated from multiple sources |
68 |
|
Lack of automation/repeatable process |
51 |
|
Metrics need to be identified or defined |
45 |
|
Production is cumbersome |
42 |
|
Data quality is not reliable |
36 |
|
Sharing findings is challenging |
21 |
|
Analytic tools are too complex |
17 |
|
Ensuring security and integrity of workforce data |
17 |
|
Other |
3 |
a) Compute the percentage of values in each response need.
b) What conclusions can you reach concerning technical barriers to workforce analytics?
c) Construct a bar chart, a pie chart, and a Pareto chart.
d) What conclusions can you reach concerning technical barriers to workforce analytics?
2.15 & 2.36) The NBACost dataset contains the total cost ($) for four average priced tickets, two beers, four soft drinks, four hot dogs, two game programs, two adult-sized caps, and one parking space at each of the 30 National Basketball Association (NBA) arenas during the 2014-2015 season. These costs were:
|
246.39 |
444.16 |
404.60 |
212.40 |
477.32 |
271.74 |
312.20 |
322.50 |
|
261.20 |
336.52 |
369.86 |
232.44 |
435.72 |
541.00 |
223.92 |
468.20 |
|
325.85 |
281.06 |
221.80 |
676.42 |
295.40 |
263.10 |
278.90 |
341.90 |
|
317.08 |
280.28 |
340.60 |
289.71 |
275.74 |
258.78 |
|
|
a) Organize these costs as an ordered array.
b) Construct a frequency distribution and a percentage distribution based on quartile classes and median position.
c) Construct a histogram and percentage polygon
d) Around what value, if any, are the costs of attending a basketball game concentrated? Explain.
2.16 & 2.38) The Utility dataset contains the following data about the cost of electricity (in $) during July 2015 for a random sample of 50 one-bedroom apartments in a large city.
|
96 |
171 |
202 |
178 |
147 |
102 |
153 |
197 |
127 |
82 |
|
157 |
185 |
90 |
116 |
172 |
111 |
148 |
213 |
130 |
165 |
|
141 |
149 |
206 |
175 |
123 |
128 |
144 |
168 |
109 |
167 |
|
95 |
163 |
150 |
154 |
130 |
143 |
187 |
166 |
139 |
149 |
|
108 |
119 |
183 |
151 |
114 |
135 |
191 |
137 |
129 |
158 |
a) Construct a frequency distribution and a percentage distribution that have class intervals with the upper-class boundaries $99, $119, and so on.
b) Construct a cumulative percentage distribution.
c) Around what amount does the monthly electricity cost seem to be concentrated?
d) Construct a histogram and a percentage polygon.
e) Construct a cumulative percentage polygon.
f) Around what amount does the monthly electricity cost seem to be concentrated? Explain.
2.25) How much time doing what activities do college students spend using their cell phones? A 2014 Baylor University study showed college students spend an average of 9 hours a day using their cell phones across a range of activities. Use CellPhoneActivity dataset.
a) Construct a bar chart, a pie chart, and a Pareto chart.
b) Which graphical method do you think is best for portraying these data?
c) What conclusions can you reach concerning how college students spend their time using cellphones?
2.40) The PropertyTaxes dataset contains data about the property taxes per capita ($) for the 50 states and the District of Columbia. Use this dataset to:
a) Construct a histogram and a cumulative percentage polygon to visualize the data.
b) What conclusions can you reach concerning the property taxes per capita?
2.52) College football is big business, with coaches’ pay in millions of dollars. The CollegeFootball dataset contains the 2013 total athletic department revenue and 2014 football coaches’ total pay for 108 schools. Use this data set to answer:
a) Do you think schools with higher total revenues also have higher total coaches’ pay?
b) Construct a scatter plot with total revenue on the X axis and total coaches’ pay on the Y axis.
c) Does the scatter plot confirm or contradict your answer to (a)?
d) Compute the covariance.
e) Compute the coefficient of correlation.
f) Based on (d) & (e), what conclusions can you reach about the relationship between coaches’ total pay and athletic department revenues?
2.55) The data in NewHomeSales represent number and median sales price of new single-family houses sold in the United States recorded at the end of each month from January 2000 through December 2014.
a) Construct a time-series plot of new home sales prices.
b) What pattern, if any, is present in the data?
2.96) The DomesticBeer dataset contains the percentage alcohol, number of calories per 12 ounces, and number of carbohydrates (in grams) per 12 ounces for 158 of the best-selling domestic beers in the United States.
a) Construct a percentage histogram for percentage alcohol, number of calories per 12 ounces, and number of carbohydrates (in grams) per 12 ounces.
b) Construct three scatter plots: percentage alcohol versus calories, percentage alcohol versus carbohydrates, and calories versus carbohydrates
c) Discuss inferences you have developed based on the visualization provided in (a) and (b).
2.97 & 3.41) The CigaretteTax dataset contains the state cigarette tax ($) for each state as of January 1, 2015.
a) Organize the data into an ordered array.
b) Plot a percentage histogram.
c) What conclusions can you reach about the differences in the state cigarette tax between the states?
d) Compute the population mean and population standard deviation for the state cigarette tax.
e) How do interpret these parameters from (d).
3.1) The following set of data is from a sample of n=5: 7 4 9 8 2
a) Compute the mean, median, and mode
b) Compute the range, variance, standard deviation, and coefficient of variation
c) Compute the Z scores. Are there any outliers?
d) Describe the shape of the data set.
|
Descriptive Statistics |
|
|
|
Descriptors |
Values |
|
|
Mean |
|
|
|
Median |
|
|
|
Mode |
|
|
|
Range |
|
|
|
Variance |
|
|
|
Standard Deviation |
|
|
|
Coefficient of Variation |
|
|
|
Z-Score |
Z-Score |
Outlier, Yes (1,0) |
|
|
|
|
|
Skewness Shape |
|
|
|
Apex Shape |
|
3.16 & 3.33) The HotelAway dataset contains the average room price (in US$) paid by various nationalities while traveling abroad (away from their home country) in 2014.
a) Compute the mean, median, and mode
b) Compute the range, variance, standard deviation, and coefficient of variation
c) Compute the Z scores. Are there any outliers?
d) Construct a boxplot and describe the shape of the data set.
e) Based on the results of the table, what conclusions can you reach concerning the room price (in US$) in 2014?
|
Descriptive Statistics |
|
|
|
Descriptors |
Values |
|
|
Mean |
|
|
|
Median |
|
|
|
Mode |
|
|
|
Range |
|
|
|
Variance |
|
|
|
Standard Deviation |
|
|
|
Coefficient of Variation |
|
|
|
Z-Score |
Z-Score |
Outlier, Yes (1,0) |
|
|
|
|
|
Skewness Shape |
|
|
|
Apex Shape |
|
3.71) The Protein dataset contains the cost per meal and the ratings of 50 center city and 50 metro area restaurants on their food, décor, and service (and their summated ratings). Complete the following for the center city and metro area restaurants:
a) Construct a boxplot of the cost of a meal. What is the shape of the distribution?
b) Compute and interpret the correlation coefficient of the summated rating and the cost of a meal.
c) What conclusions can you reach about the cost of a meal at center city and metro area restaurants?
3.73) What was the mean price of a room at two-star, three-star, and four-star hotels in the major cities of the world during the first half of 2014? The HotelPrices contains the mean prices in English pounds, approximately US$1.57 as of May 2015, for the three categories of hotels. Do the following for two-star, three-star, and four-star hotels:
a) Compute the mean, median, first quartile, and third quartile.
b) Compute the range, interquartile range, variance, standard deviation, and coefficient of variation.
c) Interpret the measures of central tendency and variation within the context of this problem.
|
Descriptive Statistics |
|
|
Descriptors |
Values |
|
Mean |
|
|
Median |
|
|
Mode |
|
|
First Quartile |
|
|
Third Quartile |
|
|
Range |
|
|
Interquartile Range |
|
|
Variance |
|
|
Standard Deviation |
|
|
Coefficient of Variation |
|
d) Construct a boxplot. Are the data skewed? If so, how?
e) Compute the covariance between the mean price at two-star and three-star hotels, between two-star and four-star hotels, and between three-star and four-star hotels.
g) Which do you think is more valuable in expressing the relationship between the mean price of a room at two-star, three-star, and four-star hotels – the covariance or the coefficient of correlation? Explain.
h) Based on (f), what conclusions can you reach about the relationship between the mean price of a room at two-star, three-star, and four-star hotels?
1
__MACOSX/Stat HW #1/._Problem Sets 1.docx
Stat HW #1/NewHomeSalestxt.csv
Period,Homes Sold (thousands),Mean Sales Price ($thousands) Jan-2000,67,163.5 Feb-2000,78,162.4 Mar-2000,88,165.1 Apr-2000,78,162.6 May-2000,77,164.7 Jun-2000,71,160.1 Jul-2000,76,169 Aug-2000,73,166.6 Sep-2000,70,171.5 Oct-2000,71,176.3 Nov-2000,63,174.7 Dec-2000,65,162 Jan-2001,72,171.3 Feb-2001,85,169.1 Mar-2001,94,166.3 Apr-2001,84,175.2 May-2001,80,175.3 Jun-2001,79,179.4 Jul-2001,76,175 Aug-2001,74,173.7 Sep-2001,66,166.4 Oct-2001,66,171.3 Nov-2001,67,168.1 Dec-2001,66,180.2 Jan-2002,66,187.1 Feb-2002,84,191.1 Mar-2002,90,183.4 Apr-2002,86,187.1 May-2002,88,181 Jun-2002,84,190.6 Jul-2002,82,175.6 Aug-2002,90,178.9 Sep-2002,82,177.5 Oct-2002,77,189.2 Nov-2002,73,181.2 Dec-2002,70,197.6 Jan-2003,76,181.7 Feb-2003,82,187 Mar-2003,98,185.1 Apr-2003,91,189.5 May-2003,101,195.5 Jun-2003,107,187.9 Jul-2003,99,190.2 Aug-2003,105,190.5 Sep-2003,90,192 Oct-2003,88,194.1 Nov-2003,76,207.1 Dec-2003,75,196 Jan-2004,89,209.5 Feb-2004,102,219.6 Mar-2004,123,209.6 Apr-2004,109,222.3 May-2004,115,211.7 Jun-2004,105,215.7 Jul-2004,96,212.4 Aug-2004,102,218.1 Sep-2004,94,211.6 Oct-2004,101,229.2 Nov-2004,84,224.5 Dec-2004,83,229.6 Jan-2005,92,223.1 Feb-2005,109,237.3 Mar-2005,127,229.3 Apr-2005,116,236.3 May-2005,120,228.3 Jun-2005,115,226.1 Jul-2005,117,229.2 Aug-2005,110,240.1 Sep-2005,99,240.4 Oct-2005,105,243.9 Nov-2005,86,237.9 Dec-2005,87,238.6 Jan-2006,89,244.9 Feb-2006,88,250.8 Mar-2006,108,238.8 Apr-2006,100,257 May-2006,102,238.2 Jun-2006,98,243.2 Jul-2006,83,238.1 Aug-2006,88,243.9 Sep-2006,80,226.7 Oct-2006,74,250.4 Nov-2006,71,240.1 Dec-2006,71,244.7 Jan-2007,66,254.4 Feb-2007,68,250.8 Mar-2007,80,262.6 Apr-2007,83,242.5 May-2007,79,245 Jun-2007,73,235.5 Jul-2007,68,246.2 Aug-2007,60,236.5 Sep-2007,53,240.3 Oct-2007,57,234.3 Nov-2007,45,249.1 Dec-2007,44,227.7 Jan-2008,44,232.4 Feb-2008,48,245.3 Mar-2008,49,229.3 Apr-2008,49,246.4 May-2008,49,229.3 Jun-2008,45,234.3 Jul-2008,43,237.3 Aug-2008,38,221 Sep-2008,35,225.2 Oct-2008,32,213.2 Nov-2008,27,221.6 Dec-2008,26,229.6 Jan-2009,24,208.6 Feb-2009,29,209.7 Mar-2009,31,205.1 Apr-2009,32,219.2 May-2009,34,222.3 Jun-2009,37,214.7 Jul-2009,38,214.2 Aug-2009,36,207.1 Sep-2009,30,216.6 Oct-2009,33,215.1 Nov-2009,26,218.8 Dec-2009,24,222.6 Jan-2010,24,218.2 Feb-2010,27,221.9 Mar-2010,36,224.8 Apr-2010,41,208.3 May-2010,26,230.5 Jun-2010,28,219.5 Jul-2010,26,212.1 Aug-2010,23,226.6 Sep-2010,25,228 Oct-2010,23,204.2 Nov-2010,20,219.6 Dec-2010,23,241.2 Jan-2011,21,240.1 Feb-2011,22,220.1 Mar-2011,28,220.5 Apr-2011,30,224.7 May-2011,28,222 Jun-2011,28,240.2 Jul-2011,27,229.9 Aug-2011,25,219.6 Sep-2011,24,217 Oct-2011,25,224.8 Nov-2011,23,214.3 Dec-2011,24,218.6 Jan-2012,23,221.7 Feb-2012,30,239.9 Mar-2012,34,239.8 Apr-2012,34,236.4 May-2012,35,239.2 Jun-2012,34,232.6 Jul-2012,33,237.4 Aug-2012,31,253.2 Sep-2012,30,254.6 Oct-2012,29,247.2 Nov-2012,28,245 Dec-2012,28,258.3 Jan-2013,32,251.5 Feb-2013,36,265.1 Mar-2013,41,257.5 Apr-2013,43,279.3 May-2013,40,263.7 Jun-2013,43,259.8 Jul-2013,33,262.2 Aug-2013,31,255.3 Sep-2013,31,269.8 Oct-2013,36,264.3 Nov-2013,32,277.1 Dec-2013,31,275.5 Jan-2014,33,269.8 Feb-2014,35,268.4 Mar-2014,39,282.3 Apr-2014,39,274.5 May-2014,43,285.6 Jun-2014,38,287 Jul-2014,35,280.4 Aug-2014,36,291.7 Sep-2014,37,261.5 Oct-2014,38,299.4 Nov-2014,31,298.3 Dec-2014,34,302.1
__MACOSX/Stat HW #1/._NewHomeSalestxt.csv
Stat HW #1/HotelPricestxt.csv
City,Two-Star (?),Three-Star (?),Four-Star(?) Amsterdam,76,89,117 Bangkok,18,26,56 Barcelona,70,89,105 Beijing,31,44,76 Berlin,60,61,79 Boston,89,132,181 Brussels,71,77,95 Cancun,52,85,206 Chicago,66,111,132 Dubai,82,82,107 Dublin,51,72,89 Edinburgh,62,87,112 Frankfurt,67,89,103 Hong Kong,43,76,124 Las Vegas,38,48,88 Lisbon,49,58,77 London,79,103,12 Los Angeles,71,110,193 Madrid,54,61,79 Miami,85,117,183 Montreal,67,102,136 Mumbai,31,47,74 Munich,59,91,110 New York,103,155,186 Orlando,44,71,128 Paris,70,96,145 Rome,64,88,101 San Francisco,91,146,187 Seattle,92,122,161 Seoul,37,76,102 Shanghai,22,49,79 Singapore,47,98,141 Sydney,38,79,121 Taipei,40,71,109 Tokyo,48,79,155 Toronto,66,87,126 Vancouver,67,98,141 Venice,87,109,133 Vienna,43,64,83 Warsaw,40,50,62 Washington,74,122,142
__MACOSX/Stat HW #1/._HotelPricestxt.csv
Stat HW #1/Cellphone_Activitytxt.csv
Cellphone Activity,Percentage Banking,2 Checking Date and Time,8 Listening to Music,12 Playing Games,4 Reading,3 Sending emails,9 Social Media,18 Surfing the Internet,7 Taking Photos,3 Talking,6 Texting,18 YouTube,2 Other,8
__MACOSX/Stat HW #1/._Cellphone_Activitytxt.csv
Stat HW #1/College_Footballtxt.csv
School,Conference,Head Coach,School Pay,Other Pay,Total Pay,Max Bonus,Athletic Department Total Revenue Air Force,Mt. West,Troy Calhoun,866250,26500,892750,247500,39031348 Akron,MAC,Terry Bowden,406000,#N/A,406000,255000,27954164 Alabama,SEC,Nick Saban,6950203,209984,7160187,700000,143776550 Alabama at Birmingham,CUSA,Bill Clark,500000,#N/A,500000,430000,28159249 Appalachian State,Sun Belt,Scott Satterfield,225000,#N/A,225000,25000,19775727 Arizona,PAC-12,Rich Rodriguez,2898500,400000,3298500,2125000,68510915 Arizona State,PAC-12,Todd Graham,2700000,2960,2702960,3491000,65673955 Arkansas,SEC,Bret Bielema,3200000,14000,3214000,700000,99770840 Arkansas State,Sun Belt,Blake Anderson,700000,#N/A,700000,235000,16281038 Army,Ind.,Jeff Monken,834667,#N/A,834667,256000,37289204 Auburn,SEC,Gus Malzahn,3850000,4500,3854500,1400000,103680609 Ball State,MAC,Pete Lembo,500000,3500,503500,450000,21315999 Boise State,Mt. West,Bryan Harsin,1000004,#N/A,1000004,400000,43166257 Bowling Green,MAC,Dino Babers,405000,0,405000,130000,23611448 Buffalo,MAC,Jeff Quinn,380210,0,380210,470000,28964050 California,PAC-12,Sonny Dykes,1800000,8000,1808000,304000,94487380 Central Florida,AAC,George O'Leary,1800000,0,1800000,750000,41222301 Central Michigan,MAC,Dan Enos,360000,0,360000,305000,27680624 Cincinnati,AAC,Tommy Tuberville,2200000,0,2200000,565000,61915431 Clemson,ACC,Dabo Swinney,3150000,25100,3175100,1200000,69061398 Colorado,PAC-12,Mike MacIntyre,2008500,1650,2010150,1500000,58334345 Colorado State,Mt. West,Jim McElwain,1500000,#N/A,1500000,150000,34791926 Connecticut,AAC,Bob Diaco,1500000,#N/A,1500000,450000,63336022 East Carolina,AAC,Ruffin McNeill,1250000,2500,1252500,550000,35805232 Eastern Michigan,MAC,Chris Creighton,425000,#N/A,425000,310000,27797656 Florida,SEC,Will Muschamp,2724500,6500,2731000,454000,130011244 Florida Atlantic,CUSA,Charlie Partridge,500000,#N/A,500000,100000,24538411 Florida International,CUSA,Ron Turner,550000,1000,551000,150000,28332261 Florida State,ACC,Jimbo Fisher,3591667,0,3591667,1275000,91382441 Fresno State,Mt. West,Tim DeRuyter,1400000,35000,1435000,1685000,33734773 Georgia,SEC,Mark Richt,3200000,114000,3314000,1000000,98120889 Georgia Southern,Sun Belt,Willie Fritz,400000,#N/A,400000,42500,13200750 Georgia State,Sun Belt,Trent Miles,510000,#N/A,510000,381500,26721964 Georgia Tech,ACC,Paul Johnson,2588000,2500,2590500,1275000,61780812 Hawaii,Mt. West,Norm Chow,550000,70500,620500,1180000,37017100 Houston,AAC,Tony Levine,1025000,300,1025300,422500,42024887 Idaho,Sun Belt,Paul Petrino,400011,0,400011,152000,19593192 Illinois,Big Ten,Tim Beckman,1950000,250,1950250,80000,79725521 Indiana,Big Ten,Kevin Wilson,1301644,#N/A,1301644,1130000,76660265 Iowa,Big Ten,Kirk Ferentz,4075000,0,4075000,1750000,107153782 Iowa State,Big 12,Paul Rhoads,1800000,8025,1808025,1050000,62357761 Kansas,Big 12,Charlie Weis,2500000,0,2500000,765000,93114168 Kansas State,Big 12,Bill Snyder,2900000,0,2900000,580000,70457283 Kent State,MAC,Paul Haynes,382500,1200,383700,336000,26557674 Kentucky,SEC,Mark Stoops,2700000,1600,2701600,1475000,95720724 LSU,SEC,Les Miles,4300000,69582,4369582,700000,117457398 Louisiana Tech,CUSA,Skip Holtz,500000,10000,510000,295000,18570493 Louisiana-Lafayette,Sun Belt,Mark Hudspeth,1000000,3156,1003156,155000,18114361 Louisiana-Monroe,Sun Belt,Todd Berry,357000,1250,358250,47500,11231311 Louisville,ACC,Bobby Petrino,3000000,#N/A,3000000,1266667,96193330 Marshall,CUSA,Doc Holliday,607070,2750,609820,115000,27587274 Maryland,Big Ten,Randy Edsall,2033680,200,2033880,950000,63714470 Massachusetts,MAC,Mark Whipple,450000,#N/A,450000,200000,30060635 Memphis,AAC,Justin Fuente,1006779,0,1006779,725000,46346285 Miami (Ohio),MAC,Chuck Martin,450000,#N/A,450000,462243,28705691 Michigan,Big Ten,Brady Hoke,2854000,2000,2856000,575000,143514125 Michigan State,Big Ten,Mark Dantonio,5611845,24300,5636145,650000,97942726 Middle Tennessee State,CUSA,Rick Stockstill,801504,1625,803129,126142,27667552 Minnesota,Big Ten,Jerry Kill,2100000,#N/A,2100000,1150000,98286669 Mississippi,SEC,Hugh Freeze,3000000,18000,3018000,825000,73390050 Mississippi State,SEC,Dan Mullen,3000000,0,3000000,710000,62764025 Missouri,SEC,Gary Pinkel,3400000,0,3400000,1825000,76306889 Nebraska,Big Ten,Bo Pelini,3077646,#N/A,3077646,1000000,86916001 Nevada,Mt. West,Brian Polian,575000,0,575000,250000,26627415 Nevada-Las Vegas,Mt. West,Bobby Hauck,850000,0,850000,285000,64513044 New Mexico,Mt. West,Bob Davie,772690,11000,783690,480000,44345840 New Mexico State,Sun Belt,Doug Martin,376044,1000,377044,185000,30105460 North Carolina,ACC,Larry Fedora,1830000,#N/A,1830000,345835,82792342 North Texas,CUSA,Dan McCarney,710000,1600,711600,945000,28800436 Northern Illinois,MAC,Rod Carey,400000,1500,401500,205000,26259284 Northwestern,Big Ten,Pat Fitzgerald,2480967,#N/A,2480967,#N/A,11767380 Ohio,MAC,Frank Solich,553000,1500,554500,327000,27265061 Ohio State,Big Ten,Urban Meyer,4486640,50000,4536640,550000,139639307 Oklahoma,Big 12,Bob Stoops,5058333,0,5058333,819500,123805661 Oklahoma State,Big 12,Mike Gundy,3500000,#N/A,3500000,550000,93664337 Old Dominion,CUSA,Bobby Wilder,475000,11010,486010,503333,36929483 Oregon,PAC-12,Mark Helfrich,2000000,#N/A,2000000,1135000,115241070 Oregon State,PAC-12,Mike Riley,1510008,#N/A,1510008,369000,65467970 Penn State,Big Ten,James Franklin,4300000,#N/A,4300000,1000000,104751464 Purdue,Big Ten,Darrell Hazell,2090000,#N/A,2090000,1545000,72379392 Rutgers,Big Ten,Kyle Flood,975000,12000,987000,350000,78989475 San Diego State,Mt. West,Rocky Long,800000,2000,802000,855000,39211827 San Jose State,Mt. West,Ron Caragher,525000,0,525000,195000,25854038 South Alabama,Sun Belt,Joey Jones,435000,36000,471000,171000,21115562 South Carolina,SEC,Steve Spurrier,4000000,16900,4016900,1700000,90484422 South Florida,AAC,Willie Taggart,1150000,32000,1182000,650000,45066258 Southern Mississippi,CUSA,Todd Monken,700000,0,700000,125000,22776416 Tennessee,SEC,Butch Jones,2960000,0,2960000,1000000,111579779 Texas,Big 12,Charlie Strong,5000000,270,5000270,1000000,165691486 Texas A&M,SEC,Kevin Sumlin,5000000,6000,5006000,750000,93957906 Texas State,Sun Belt,Dennis Franchione,400000,1200,401200,232500,29764777 Texas Tech,Big 12,Kliff Kingsbury,2605000,300,2605300,1500000,72917990 Texas-El Paso,CUSA,Sean Kugler,503864,4500,508364,478481,29017848 Texas-San Antonio,CUSA,Larry Coker,400000,2150,402150,230000,23807953 Toledo,MAC,Matt Campbell,470500,12037,482537,450000,23054218 Troy,Sun Belt,Larry Blakeney,518788,0,518788,77500,19505723 UCLA,PAC-12,Jim Mora,3250000,0,3250000,930000,83926720 Utah,PAC-12,Kyle Whittingham,2200000,#N/A,2200000,740000,46855283 Utah State,Mt. West,Matt Wells,575000,1434,576434,765000,23684266 Virginia,ACC,Mike London,2298413,5186,2303599,715000,84402710 Virginia Tech,ACC,Frank Beamer,2420913,240000,2660913,382500,70030484 Washington,PAC-12,Chris Petersen,3681720,0,3681720,1175000,85072886 Washington State,PAC-12,Mike Leach,2750000,0,2750000,625000,47191240 West Virginia,Big 12,Dana Holgorsen,3080000,0,3080000,600000,77706698 Western Kentucky,CUSA,Jeff Brohm,600000,#N/A,600000,550000,27606401 Western Michigan,MAC,P.J. Fleck,392500,#N/A,392500,236000,28624348 Wisconsin,Big Ten,Gary Andersen,2200000,85000,2285000,440000,149141405 Wyoming,Mt. West,Craig Bohl,832000,0,832000,330000,29647103