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MATHMODULE-4MajorAssignment2.xlsx
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Grading Sheet

Part 1
Requirements: Answer each question fully. Use Excel formulas with cell references. Answers must be recorded on the worksheet.
Possible points Points earned Comments
Five year inflation rate 10
Projection of expenses in Major Assignment 1 10
Part 1 total 20 0
Part 2
Requirements: Answer each question fully. Use Excel formulas with cell references. Answers must be recorded on the worksheet.
Possible points Points earned Comments
Descriptive Statistics 16
Interpret Descriptive Statistics 14
Proportion calculations 10
Interpretation of proportions 10
Conversions 10
Improvement level data set 5
Descriptive Statistics for improvement levels 10
Histogram 5
Standard error of the mean 5
Confidence interval 10
Discussion of the placement of 0 10
Part 2 total 105 0
Total of Worksheet 2 125 0 0%

Part 1 Inflation

Input your name below: Color Key:
Full Name formula
text/number
You should just use the budget information from Major Assignment 1. For the CPI values, here are the instructions: Step 1: Go to the Bureau of Labor Statistics website at the link below. Step 2: Check the box next to "U.S. city average, All items - CUUR0000SA0" Step 3: Click "Retrieve Data" Use the most recent CPI value and the CPI for the same month but five years earlier to compute the 5 year inflation rate. Use this inflation rate to estimate the price of your trip five years from now.
Month Year CPI
Unadjusted CPI, all items for 5 years ago number
Unadjusted CPI, all items for last month number
What is the 5 year inflation rate, that is percent increase in your CPI values? formula
If something cost $1.00 five years ago, what would it cost now? formula
Total budget from Major Assignment 1 number
5 year projected budget total formula CPI Data Link
Like Topic 4 DQ 1

Name!

https://data.bls.gov/cgi-bin/surveymost?cu

Part 2 Questions 1-3

Enter your name in cell A2 of "Part 1 - Inflation" to generate data. Part 2 – Data Analysis: Enter your name in cell A2 of "Part 1 - Inflation" to generate data. You have just completed a mission to Sierra Leone. The goal of the mission was to improve the quality of water in the wells in a certain region. You collected data on the E. coli count from each well before and after your mission. You need to write a report on the success of the mission and for that you need to perform some statistical analysis on the data. You will be looking at the data from different perspectives to determine if the water quality has improve.
Before wells were dug E.Coli per ml After wells were dug E.Coli per ml YOUR NAME: From Part 1 A1 Full Name Original Before Data Original After Data seed
Data Data Before After 1 120 ERROR:#VALUE! ERROR:#VALUE!
Data Data min = formula min = formula Recall Topic 1 DQ 2 78 67 7582 407 ERROR:#VALUE! ERROR:#VALUE!
Data Data max = formula max = formula 1. Calculate descriptive statistics for your data in the table provided in the Excel spreadsheet. Use the statistics including mean, max and standard deviations of the data to decide if it appears if there has been improvement in water quality? This requires a thorough discussion of these statistics to obtain full marks. (Fill in the before (F3:F8) and after (H3:H8) tables to the left for the descriptive statistics. The data has been named before and after for your convenience in creating formulas.) 19 4 271 ERROR:#VALUE! ERROR:#VALUE!
Data Data mean= formula mean= formula 32 23 5652 ERROR:#VALUE! ERROR:#VALUE!
Data Data SD = formula SD = formula 125 110 2517 ERROR:#VALUE! ERROR:#VALUE!
Data Data # of wells tested = formula # of wells tested = formula 53 41 2506 ERROR:#VALUE! ERROR:#VALUE!
Data Data # of wells with 0 E coli = formula # of wells with 0 E coli = formula 68 42 9039 ERROR:#VALUE! ERROR:#VALUE!
Data Data 4 10 3784 ERROR:#VALUE! ERROR:#VALUE!
Data Data Ratio Before After Answer Question 1 below: 106 79 2961 ERROR:#VALUE! ERROR:#VALUE!
Data Data Percent Clean formula formula 38 6 6466 ERROR:#VALUE! ERROR:#VALUE!
Data Data 36 16 5191 ERROR:#VALUE! ERROR:#VALUE!
Data Data Conversions Color Key: 4 14 6152 ERROR:#VALUE! ERROR:#VALUE!
Data Data ml oz formula 17 5 5737 ERROR:#VALUE! ERROR:#VALUE!
Data Data 29.5735 1 text/number 43 9 1230 ERROR:#VALUE! ERROR:#VALUE!
Data Data 23 3 5591 ERROR:#VALUE! ERROR:#VALUE!
Data Data In 24 ounces 2. The water quality is “good” if the count of E coli is 0; otherwise, the water quality is still bad. Calculate the proportion of wells with “good” water to wells whose water is not good. From this measure does it appear that the quality of water improved? Explain and use the proportions that you calculated. (In G11 and H11 calculate the percent Clean for before and after.) 32 8 8728 ERROR:#VALUE! ERROR:#VALUE!
Data Data E.coli before E. coli after 49 28 8385 ERROR:#VALUE! ERROR:#VALUE!
Data Data formula formula 36 18 306 ERROR:#VALUE! ERROR:#VALUE!
Data Data 2 21 4043 ERROR:#VALUE! ERROR:#VALUE!
Data Data 33 22 7564 ERROR:#VALUE! ERROR:#VALUE!
Data Data Answer Question 2 below: 58 25 3457 ERROR:#VALUE! ERROR:#VALUE!
Data Data 75 59 1222 ERROR:#VALUE! ERROR:#VALUE!
Data Data 82 63 7007 ERROR:#VALUE! ERROR:#VALUE!
Data Data 80 60 5740 ERROR:#VALUE! ERROR:#VALUE!
Data Data 70 52 5285 ERROR:#VALUE! ERROR:#VALUE!
Data Data 79 50 7858 ERROR:#VALUE! ERROR:#VALUE!
Data Data 73 66 6451 ERROR:#VALUE! ERROR:#VALUE!
Data Data 3. Look at well #1 (B2 and C2) in your data. If you drank 24oz of water how many E.coli would you ingest if you drank from the well before the mission? After the mission? (In E20 and G20 calculate how many E.coli would you ingest if you drank 24 oz. of water from Well 1 before the mission and after the mission.) 76 54 3260 ERROR:#VALUE! ERROR:#VALUE!
Data Data 72 42 3989 ERROR:#VALUE! ERROR:#VALUE!
Data Data 72 55 7950 ERROR:#VALUE! ERROR:#VALUE!
Data Data 70 53 8511 ERROR:#VALUE! ERROR:#VALUE!
Data Data 84 54 5520 ERROR:#VALUE! ERROR:#VALUE!
Data Data Nothing to answer here! 81 62 7537 ERROR:#VALUE! ERROR:#VALUE!
Data Data 69 59 3650 ERROR:#VALUE! ERROR:#VALUE!
Data Data 85 52 8335 ERROR:#VALUE! ERROR:#VALUE!
Data Data 100 69 9156 ERROR:#VALUE! ERROR:#VALUE!
Data Data 70 57 6005 ERROR:#VALUE! ERROR:#VALUE!
Data Data 74 45 8826 ERROR:#VALUE! ERROR:#VALUE!
Data Data 63 39 211 ERROR:#VALUE! ERROR:#VALUE!
Data Data 76 60 7100 ERROR:#VALUE! ERROR:#VALUE!
Data Data 78 75 3037 ERROR:#VALUE! ERROR:#VALUE!
Data Data 87 64 2186 ERROR:#VALUE! ERROR:#VALUE!
Data Data 71 41 1475 ERROR:#VALUE! ERROR:#VALUE!
Data Data 83 67 3500 ERROR:#VALUE! ERROR:#VALUE!
Data Data 71 56 7369 ERROR:#VALUE! ERROR:#VALUE!
Data Data 75 57 5870 ERROR:#VALUE! ERROR:#VALUE!
Data Data 76 58 619 ERROR:#VALUE! ERROR:#VALUE!
Data Data 63 39 8260 ERROR:#VALUE! ERROR:#VALUE!
Data Data 70 38 8673 ERROR:#VALUE! ERROR:#VALUE!
Data Data 65 50 4362 ERROR:#VALUE! ERROR:#VALUE!
Data Data 83 59 1547 ERROR:#VALUE! ERROR:#VALUE!
Data Data 76 48 9100 ERROR:#VALUE! ERROR:#VALUE!
Data Data 78 59 6745 ERROR:#VALUE! ERROR:#VALUE!
Data Data 76 49 6254 ERROR:#VALUE! ERROR:#VALUE!
Data Data 68 47 6027 ERROR:#VALUE! ERROR:#VALUE!
Data Data 77 51 4932 ERROR:#VALUE! ERROR:#VALUE!
Data Data 75 58 1549 ERROR:#VALUE! ERROR:#VALUE!
Data Data 67 53 8746 ERROR:#VALUE! ERROR:#VALUE!
Data Data 74 45 5199 ERROR:#VALUE! ERROR:#VALUE!
Data Data 86 58 4736 ERROR:#VALUE! ERROR:#VALUE!
Data Data 85 67 8725 ERROR:#VALUE! ERROR:#VALUE!
Data Data 72 48 4330 ERROR:#VALUE! ERROR:#VALUE!
Data Data 73 65 7211 ERROR:#VALUE! ERROR:#VALUE!
Data Data 59 43 6320 ERROR:#VALUE! ERROR:#VALUE!
Data Data 72 55 3517 ERROR:#VALUE! ERROR:#VALUE!
Data Data 64 25 8946 ERROR:#VALUE! ERROR:#VALUE!
Data Data 67 48 6487 ERROR:#VALUE! ERROR:#VALUE!
Data Data 79 55 6060 ERROR:#VALUE! ERROR:#VALUE!
Data Data 64 33 3677 ERROR:#VALUE! ERROR:#VALUE!
Data Data 86 65 8142 ERROR:#VALUE! ERROR:#VALUE!
Data Data 74 53 2043 ERROR:#VALUE! ERROR:#VALUE!
Data Data 83 61 3856 ERROR:#VALUE! ERROR:#VALUE!
Data Data 81 55 8561 ERROR:#VALUE! ERROR:#VALUE!
Data Data 70 47 5842 ERROR:#VALUE! ERROR:#VALUE!
Data Data 71 54 5575 ERROR:#VALUE! ERROR:#VALUE!
Data Data 68 54 2388 ERROR:#VALUE! ERROR:#VALUE!
Data Data 76 64 2409 ERROR:#VALUE! ERROR:#VALUE!
Data Data 72 55 3278 ERROR:#VALUE! ERROR:#VALUE!
Data Data 71 55 803 ERROR:#VALUE! ERROR:#VALUE!
Data Data 86 77 3208 ERROR:#VALUE! ERROR:#VALUE!
Data Data 86 62 4021 ERROR:#VALUE! ERROR:#VALUE!
Data Data 88 67 2286 ERROR:#VALUE! ERROR:#VALUE!
Data Data 78 59 2119 ERROR:#VALUE! ERROR:#VALUE!
Data Data 73 45 8748 ERROR:#VALUE! ERROR:#VALUE!
Data Data 84 57 4845 ERROR:#VALUE! ERROR:#VALUE!
Data Data 78 53 3190 ERROR:#VALUE! ERROR:#VALUE!
Data Data 77 57 7207 ERROR:#VALUE! ERROR:#VALUE!
Data Data 88 68 7028 ERROR:#VALUE! ERROR:#VALUE!
Data Data 83 61 6609 ERROR:#VALUE! ERROR:#VALUE!
Data Data 70 32 2810 ERROR:#VALUE! ERROR:#VALUE!
Data Data 65 48 1091 ERROR:#VALUE! ERROR:#VALUE!
Data Data 74 52 7264 ERROR:#VALUE! ERROR:#VALUE!
Data Data 73 53 1525 ERROR:#VALUE! ERROR:#VALUE!
Data Data 79 58 3822 ERROR:#VALUE! ERROR:#VALUE!
Data Data 87 59 5407 ERROR:#VALUE! ERROR:#VALUE!
Data Data 79 57 4608 ERROR:#VALUE! ERROR:#VALUE!
Data Data 77 55 3865 ERROR:#VALUE! ERROR:#VALUE!
Data Data 66 52 2382 ERROR:#VALUE! ERROR:#VALUE!
Data Data 73 58 3471 ERROR:#VALUE! ERROR:#VALUE!
Data Data 85 63 7916 ERROR:#VALUE! ERROR:#VALUE!
Data Data 77 55 5357 ERROR:#VALUE! ERROR:#VALUE!
Data Data 4286 ERROR:#VALUE! ERROR:#VALUE!
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Data Data 3644 ERROR:#VALUE! ERROR:#VALUE!
Data Data 225 ERROR:#VALUE! ERROR:#VALUE!
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Data Data 2017 ERROR:#VALUE! ERROR:#VALUE!
Data Data 8458 ERROR:#VALUE! ERROR:#VALUE!
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Data Data 1042 ERROR:#VALUE! ERROR:#VALUE!
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Data Data 4325 ERROR:#VALUE! ERROR:#VALUE!
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Part 2 Questions 4-6

Enter your name in cell A2 of "Part 1 - Inflation" to generate data. Part 2 – Data Analysis: You have just completed a mission to Sierra Leone. The goal of the mission was to improve the quality of water in wells in a certain region. You collected data on the E. coli count from a sample of wells before and after your mission. You need to write a report on the success of the mission and for that you need to perform some statistical analysis on the data. You will be looking at the data from different perspectives to determine if the water quality has improved.
Before wells were dug E.Coli per ml After wells were dug E.Coli per ml Improvement Data: Before - After All computations use column D, the "Improvement" data.
Data Data min = formula Color Key:
Data Data max = formula formula 4. Since you collected water from the same source twice it makes sense to analyze the amount by which each well’s water quality improved. Calculate a data set that would measure the change in the number of E. coli in each well, and the descriptive statistics for that data set, including both the standard deviation and standard error (SE) for the data set. (see section 3.5 of the textbook and the additional Excel document provided along with this major assignment). Make a frequency distribution and histogram for your data. (Calculate the improvement in the water quality of each well in column D. (Difference in Level of e. Coli.) Then, fill out the two tables to the left and make a histogram of the improvement levels. (NOTE: The Standard deviation of this data set is not the same as the standard error. Use the formulas from section 3.5 of the text to calculate the standard error of the means.))
Data Data mean= formula text/number
Data Data SD = formula
Data Data count = formula
Data Data SE = formula
Data Data
Data Data Histogram - Width
Data Data Bin Width formula
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Data Data Frequency Distribution
Data Data Low High Bins Cumulative Frequency Frequency 5. You have calculated one sample of wells and their improvement levels. If you could take all possible samples of wells of the, the distribution of all of those sample means would be a normal distribution. (see section 3.5). Find the 95% confidence interval of that distribution, using your sample mean as the population mean and the standard error of your sample as the population standard deviation. (Calculate the 95% confidence interval of the sampling distribution in cells F30 and H30.)
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula 6. Recall that a 95% confidence interval shows a range of values that is 95% likely to contain the true value of a parameter. For example, if your 95% confidence interval for the average improvement level is (14, 18), it would mean that the true average improvement level is 95% likely to be in the range (14, 18). (See the additional Excel document provided along with this major assignment for more details.) Now answer each of these questions: a) Look at the mean for your improvement data, based on this what would you say about the change in the quality of water in the tested wells. b) Look at the confidence interval you calculated--is the value 0 inside or outside your confidence interval? c) If the value 0 is outside and less than your 95% confidence interval, can you conclude (with 95% confidence) that the water became cleaner? d) What if the value 0 were inside your confidence interval? Could you conclude (with 95% confidence) that the water became cleaner?
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data formula formula auto formula formula
Data Data Remember to create the Histogram.
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Data Data 95% Confidence Interval
Data Data Lower number to Higher number
Data Data formula to formula
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Data Data Answer Question 6 (a):
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Data Data Answer Quastion 6 (b):
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Data Data Answer Question 6 (c):
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Data Data Answer Question 6 (d):
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Histogram