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Benchmark – Ethical Behavior of Business Students at Bo Diddley Tech

During the global recession of 2008 and 2009, there were many accusations of unethical behavior by Wall Street executives, financial managers, and other corporate officers. At that time, an article appeared that suggested that part of the reason for such unethical business behavior may have stemmed from the fact that cheating had become more prevalent among business students, according to a February 10, 2009, article in the Chronicle of Higher Education. The article reported that 56% of business students admitted to cheating at some time during their academic career as compared to 47% of nonbusiness students.

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Cheating has been a concern of the dean of the college of business at Bo Diddley Tech (BDT) for several years. Some faculty members in the college believe that cheating is more widespread at BDT than at other universities, whereas other faculty members think that cheating is not a major problem in the college. To resolve some of these issues, the dean commissioned a study to assess the current ethical behavior of business students at BDT. As a former college athlete herself, the dean believed that the spirit of fair play students develop as part of participating in athletics would make them less likely to cheat. As part of this study, an anonymous exit survey was administered to a sample of 240 students from this year’s graduating class, half of whom were business students and half of whom were not. The survey asked various questions, including the student’s college and if the student was an athlete or not. Responses of the various questions were fed into a computer algorithm that made a quantitative determination as to whether the student should be considered a “cheater” or not. The results are in the attached Excel spreadsheet, “Benchmark –  Bo Diddley Tech Data Set.” 

Prepare a managerial report as part of your submission to the dean of the college that summarizes your assessment of the nature of cheating at BDT. Be sure to include the following items in your written report.

Utilize the data set provided by the instructor in the Excel spreadsheet, “Benchmark – Bo Diddley Tech Data Set” (60 records per student).

Submit the Excel data calculations (Alpha 0.05).

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  1. Make a pivot table with: Business Student (Rows), Athlete (Rows), Cheated (Columns), and Cheated (Summed Value).
  2. Create a bar chart showing cheating by athletes and business students.
  3. Determine if there is a statistical difference between nonathlete BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
  4. Determine if there is a statistical difference between athlete BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
  5. Determine if there is a statistical difference between BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
  6. Determine if there is a statistical difference between BDT nonbusiness students and the national average for nonbusiness students as reported by the Chronicle of Higher Education.

Utilizing the data you have analyzed, write a managerial report of 500-800 words for the dean. The managerial report needs to include an introduction, analysis, conclusion, and a minimum of three supporting references.

  1. Introduction (Define): Explain, in your own words, why you are providing this report and the problem(s) you are trying to solve.
  2. Collect: Describe the data set you used.
  3. Organize: Describe your pivot table.
  4. Visualize: Include and describe your bar chart.
  5. Analyze: Provide a summary of your conclusions based on the four population proportion hypothesis tests.
  6. Ethical Summary: The dean has expressed a concern related to the amount of cheating currently taking place at BDT and has strongly suggested that you “tweak” the statistical data such that they favor the image of the university. Discuss the potential use of unethical manipulation of statistical data to provide a biased outcome as well as the ethical counter proposal you would offer the dean in this scenario.
  7. Conclusion: What advice would you give to the dean based upon your analysis of the data?

You are required to submit your Excel data analysis along with your written report.

Prepare this assignment according to the guidelines found in the APA Style

2

>Student Instructions BM Overview – Benchmark College Ath

lete Cheated Sort <-- First, Sort data from Smallest to Largest No

n_

Bus

Ath

Yes 0

.692235

1

72

You and your group has been given a dataset containing 1440 records, located in the

Student_BM

tab of this spreadsheet. Bus

Non_Ath

Yes

0.1477297295 Each student is only responsible for analyzing 60 of these record records – the specifics of which will be assigned by the instructor. Non_Bus

Non_Ath Yes

0.1352892577 It is important that each student has a unique 60 records, as the results will be an input into the CLC, and duplication of

Bus Ath Yes

0.8356235029 results is not helpful. Note that the data have been randomized, so the data given to your group are likely different than the

Bus Non_Ath No

0.6910912146 data given to other groups.

Non_Bus Non_Ath No

0.9798737062 Non_Bus Ath No

0.5051422105 The intent of this assignment is for students to organize their data using a pivot table, get a graphical understanding

Bus Non_Ath No

0.1318733709 of the data through a bar chart, then do hypothesis testing comparing Bo Diddley Tech results versus the national average.

Bus Ath Yes

0.220669 Bus Ath Yes

0.8575373641 ***Note: First, Sort the data from Smallest to Largest to change the order of the dataset.***

Bus Non_Ath Yes

0.3389846151 Non_Bus Ath No

0.6215536002 All of your analysis should be done in the Student_BM tab of this spreadsheet and submitted as part of the assignmemt.

Non_Bus Ath Yes

0.257997253 The locations where the pivot table, bar chart, and relevant information should be placed in the Student_BM tab are

Non_Bus Ath Yes

0.4550064497 indicated by RED instructions. Copy the first 60 data points including the labes of Column D, E, and F. Paste data in Student_BM Worsheet

Bus Ath Yes

0.0995714894 Bus Ath No

0.8325132369 Once completed, the Student_BM tab will serve as the basis for writing your management report. It is expected that any

Non_Bus Non_Ath No

0.8437333047 conclusions you draw in the management report will be consistent with the data and analyses contained in the spreadsheet.

Bus Non_Ath Yes

0.1531775332 Non_Bus Ath Yes

0.9617615587 Instructions for Data Analysis Component:

Bus Ath Yes

0.1429279751 Non_Bus Ath Yes

0.6531232935 1. Make a pivot table with:

Business

Student (Rows),

Athlete

(Rows), Cheated (Columns), and Cheated (Summed Value).

Non_Bus Non_Ath No

0.7382068979 Non_Bus Ath Yes

0.8721928826 2. Create a bar chart showing cheating by athletes and business students.

Bus Non_Ath Yes

0.5530174952 Non_Bus Ath Yes

0.4420880941 4. Determine if there is a statistical difference between nonathlete BDT business students and the national average for business

Non_Bus Ath Yes

0.1214495903 students

as

reported by the Chronicle of Higher Education.

Non_Bus Non_Ath Yes

0.4033089744 Bus Non_Ath No

0.1036441272 5. Determine if there is a statistical difference between athlete BDT business students and the national average for business

Non_Bus Ath No

0.0666437393 students as reported by the Chronicle of Higher Education.

Bus Ath Yes

0.2625236238 Bus Ath No

0.0656776338 6. Determine if there is a statistical difference between BDT business students and the national average for business students

Non_Bus Non_Ath Yes

0.9647239894 as reported by the Chronicle of Higher Education. Bus Ath Yes

0.152436333 Bus Ath No

0.9341312536 7. Determine if there is a statistical difference between BDT nonbusiness students and the national average for nonbusiness

Non_Bus Non_Ath No

0.6243300755 students as reported by the Chronicle of Higher Education. Non_Bus Ath No

0.7893064476 Non_Bus Ath No

0.4691500426 Bus Ath Yes

0.5593968686 Instructions for Data Interpretation Component:

Bus Non_Ath No

0.3768807721 Non_Bus Ath No

0.3028585086 Utilizing the data you have analyzed, write a managerial report of 500-800 words to the dean. The managerial report needs to

Bus Non_Ath Yes

0.7664403994 include an introduction, analysis, conclusion, and a minimum of three supporting references.

Non_Bus Ath No

0.0381309134 Non_Bus Non_Ath No

0.5500401536 1. Introduction (Define):

Exp

lain, in your own words, why you are providing this report and the problem(s) you are trying to solve.

Bus Ath Yes

0.2891315924 Bus Ath Yes

0.5212439715 2. Collect: Describe the data set you used.

Bus Non_Ath Yes

0.3084625559 Non_Bus Ath No

0.9106517304 3. Organize: Describe your pivot table.

Bus Ath Yes

0.4651078517 Bus Non_Ath Yes

0.898065428 4. Visualize: Include and describe your bar chart.

Bus Non_Ath No

0.551227394 Bus Ath No

0.7789533395 5. Analyze: Provide a summary of your conclusions based upon the four population proportion hypothesis tests.

Bus Non_Ath Yes

0.9685596087 Non_Bus Ath Yes

0.6215394651 6. The dean has expressed a concern related to the amount of cheating currently taking place at Bo Diddley Tech and has strongly

Bus Ath No

0.7739781104 suggested that you “tweak” the statistical data such that they favor the image of the university.

Bus Ath No

0.6984346676 Discuss the potential use of unethical manipulation of statistical data to provide a biased outcome as well as the ethical counter

Non_Bus Non_Ath Yes

0.3531075953 proposal you would offer the dean in this scenario.

Bus Non_Ath No

0.7446084587 Non_Bus Ath No

0.9737248624 7.

Conclusion:

What advice would you give to the dean based upon your analysis of the data?

Bus Non_Ath No

0.3743720719 Bus Non_Ath Yes

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0.004833507 Bus Ath Yes

0.0962344907 Bus Non_Ath Yes

0.630184112 Non_Bus Ath Yes

0.8324885369 Non_Bus Ath Yes

0.1379631797 Bus Non_Ath No

0.5015536544 Bus Non_Ath Yes

0.3449189187 Non_Bus Non_Ath No

0.6054903113 Bus Ath No

0.4302455689 Bus Ath Yes

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0.2019826856 Bus Ath Yes

0.8545904289 Bus Ath Yes

0.7284259963 Bus Non_Ath Yes

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0.8629054428 Bus Ath Yes

0.549368902 Bus Non_Ath Yes

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86459824 Non_Bus Non_Ath No

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84954446 Non_Bus Non_Ath No

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0.7987842284 Non_Bus Ath Yes

0.8754283845 Non_Bus Ath Yes

0.2116573647 Bus Non_Ath Yes

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0.1993561722

Student_BM

College Athlete Cheated

Business

vs. National Average

Proportion Proportion Proportion

Sample Size (n)
=count(range) Sample Size (n)
=count(range) Sample Size (n)
=count(range)

Cheated Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated

Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI)

Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n

Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po

0.56 Hypothesized 0.56 Hypothesized 0.56 Hypothesized 0.47

0.95 Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95

0.05 Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05

Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0!

ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0!

Accept or Reject: Right Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0!

ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0!

ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0!

ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0!

Accept or Reject p-value: Right Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0!

ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0!

ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0!

1. Pivot Table Nationwide Average % Cheated
Insert pivot table in this cell – F2 56%
Nonbusiness 47%
2. Bar Chart
Bar chart starts in this cell – F20
Insert the appropriate numbers into the hypothesis testing calculations below based upon your pivot table results. Note the results.
3-6 Hypothesis Test
Business

Nonathlete Business Athlete vs. National Average Business vs. National Average Nonbusiness vs. National Average
Proportion
Sample Size (n)
=count(range)
Response of Interest (ROI)
Count for Response (CFR)
=COUNTIF(range,ROI)
Sample Proportion (pbar)
=CFR/n
Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po
Hypothesized
Confidence Coefficient (Coe)
Level of Significance (alpha)
=1-Coe
Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n)		 ERROR:#DIV/0!
Test Statistic (Z-stat)
=(pbar-Hypo)/StdError
Accept or Reject: Left Tail
Accept or Reject: Right Tail
Accept or Reject: Two Tail
p-value (Lower Tail)
=NORM.S.DIST(z,TRUE)
p-value (Upper Tail)
=1-LowerTail
p-value (Two Tail)
=2*MIN(LowerTail,UpperTail)
Accept or Reject p-value: Left Tail
Accept or Reject p-value: Right Tail
Accept or Reject p-value: Two Tail
p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n)
p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n)

Student Instructions CLC

Overview – Benchmark
You and your group has been given a data set containing 1440 records, located in the Student_BM tab of this spreadsheet. As part of benchmark assignment, you reached various conclusions based upon data sets of 60 records. Now here in the CLC portion, your group will recreate the pivot table, bar graph, and hypothesis tests. But this time all your member’s data records will be used, not just 60 records. Also, an additional hypothesis test has been added comparing samples taken at Bo Diddley Tech against each other, not just against the national standard. Finally, a 2 x 2 test of independence has been added. The intent of the CLC assignment is to provide insight into the value of larger data sets, and to determine if conclusions reached with the 60 record data sets hold up when more data are available for analysis.

Similar to the benchmark assignment, all your group’s analysis should be done in the

Student_CLC

tab of this spreadsheet and

submitted as part of the assignment. The locations where the pivot table, bar chart, and relevant hypothesis test and TOI information should be placed are in the Student_CLC tab indicated by RED instructions. Once completed, the Student_CLC tab will serve as the basis for preparing your management presentation. It is expected that any conclusions you draw in the management presentation will be consistent with the data and analyses contained in the spreadsheet. Instructions for Data Analysis Component:
Submit the group’s Excel data calculations (Alpha 0.05). The full data set will include each student’s combined dataset. This combined dataset will be the basis for all CLC calculations and analysis. 1. Each group member should refresh their memories regarding the conclusions they reached on the benchmark assignment. 2. As a group, make a pivot table with: Business Student (Rows), Athlete (Rows), Cheated (Columns), and Cheated (Summed Value). 3. As a group, create a bar chart showing cheating by athletes and business students. 4. Determine if there is a statistical difference between nonathlete BDT business students and the national average for business
students as reported by the Chronicle of Higher Education.
5. Determine if there is a statistical difference between athlete BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
6. Determine if there is a statistical difference between BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
7. Determine if there is a statistical difference between BDT nonbusiness students and the national average for nonbusiness

students as reported by the Chronicle of Higher Education.

8. Determine if there is a statistical difference between BDT business student athletes and nonathletes. 9. Determine if there is a statistical difference among business students, nonbusiness students, athletes and nonathletes at BDT by conducting a 2×2 chi-square test of

independence. Instructions for Data Interpretation Component:
As a group, prepare and submit one group PowerPoint presentation (8-12 slides) based upon the analysis and conclusions from the group’s analysis of all combined data records. It should include the following (Include Speaker Notes): 1. Introduction Slide (Define): Include Loom link. 2. Collect Slide: Describe the data set you used. 3. Organize Slide: Include your pivot table. 4. Visualize Slide: Include and describe your bar chart. 5. Analyze Slides: Provide a summary of your conclusions based upon the five population proportion hypothesis tests and test of independence.
6. Ethical Summary Slide : The dean has expressed concern related to the amoung of cheating at Bo Diddley Tech and has strongly suggested that you “tweak” the statistical data such that it favors the image of the university. In one slide, discuss the potential use of unethical manipulation of statistical data to provide a biased outcome as well as the ethical counter proposal you would offer the dean. 7. Conclusion Slide(s): Describe your conclusions and recommendations to the dean. After creating your group PowerPoint, create a video presentation (4-5 minutes) that showcases each of the required elements. Professional business attire is expected. Each group member must record video and present one of the required elements.

Student_CLC

College Athlete Cheated 1. Pivot Table Nationwide Average % Cheated
Insert pivot table in this cell – F2 Business 56%
Nonbusiness 47%

2. Bar Chart
Bar chart starts in this cell – F20

Insert the appropriate numbers into the hypothesis testing calculations below based upon your pivot table results. Note the results.

Business Nonathlete vs. National Average Business Athlete vs. National Average Business vs. National Average Nonbusiness vs. National Average

Proportion Proportion Proportion Proportion

Sample Size (n)
=count(range) Sample Size (n)
=count(range) Sample Size (n)
=count(range) Sample Size (n)
=count(range) Athlete Nonathlete
Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated

Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI) Response of Interest (ROI)
Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n

ERROR:#DIV/0! ERROR:#DIV/0!

Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po

Hypothesized 0.56 Hypothesized 0.56 Hypothesized 0.56 Hypothesized 0.47 Highlight your H0 and Ha

Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95

0

Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05

0.05

ERROR:#DIV/0!

Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0!

Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0!

ERROR:#DIV/0!

Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0!

ERROR:#DIV/0!

Accept or Reject: Right Tail ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0!

ERROR:#DIV/0!

Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0!

Accept or Reject: Right Tail ERROR:#DIV/0!

p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0!

p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0!

p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0!

ERROR:#DIV/0!

Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0!
Accept or Reject p-value: Right Tail ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0!
Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0!

Accept or Reject p-value: Right Tail ERROR:#DIV/0!

p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0!

p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0!

students, place the relevant numbers in the purple area of the table below and note the conclusion.

Business Non-Business

Exp Obs Exp

ERROR:#DIV/0! ERROR:#DIV/0!

Athlete ERROR:#DIV/0! ERROR:#DIV/0! 0 ERROR:#DIV/0! ERROR:#DIV/0!

ERROR:#DIV/0! ERROR:#DIV/0! 0

Total 0 0 0

ERROR:#DIV/0!

0.05

2

2

1

ERROR:#DIV/0!

Conclusion: ERROR:#DIV/0!
3-7 Hypothesis Test
Business Athlete vs. Business Nonathlete
p1 and p2 Proportion
Sample Size (n1 or n2)
=COUNT(range)
Count for Response (CFR)
=COUNTIF(n1or2,ROI)
Sample Proportion (p1 or p2)
=CFR1or2/n1or2
Two Tail H0: p1-p2=0
Left Tail H0: p1-p2≥0
Right Tail H0: p1-p2≤0		 Ha: p1-p2≠0
Ha: p1-p2<0 Ha: p1-p2>0
Hypothesized Value
Level of Sig. α
Point Estimation of Difference (Point)
=p1-p2
Pooled Estimation of p (PE)
=(n1*p1+n2*p2)/(n1+n2)
Standard Error (StdError)
=SQRT(PE*(1-PE)*(1/n1+1/n2))
Test Statistic Z-stat
=(Point-Hypo)/StdError
p-value (Lower Tail)
=NORM.S.DIST(Zstat,TRUE)
Based upon the count of cheaters, categorized into Business and

Non-Business
8. Test of Independence – Is cheating independent of College and athletic participation?
Comparing business and non-business students number of cheaters for athletes and non-athletes
Ho: all groups cheat at the same rate
Ha: there is a difference in cheating based upon College or Athletic participation
Independent Variable
Calculations
Dependent variable Obs Total
Non-Athlete
Chi square Test statistic =
Level of signicance
# of rows
# of columns
df = df = (rows – 1)(columns – 1)
p-factor =
Chi square critical 3.8415

Student Instructions BM

Overview – Benchmark

Your group has been given a dataset containing 240 records, located in the

Student_BM

tab of this spreadsheet..

Each student is only responsible for analyzing 60 of these record records – the specifics of which will be assigned by the instructor. It is important that each student has a unique 60 records, as the results will be an input into the CLC, and duplication of results is not helpful. Note that the data have been randomized, so the data given to your group are likely different than the data given to other groups. The intent of this assignment is for students to organize their data using a pivot table, get a graphical understanding of the data through a bar chart, then do hypothesis testing comparing Bo Diddly Tech results versus the national average. All of your analysis should be done in the Student_BM tab of this spreadsheet and submitted as part of the assignmemt. The location where the pivot table, bar chart, and relevant information should be placed in the Student_BM tab is indicated by RED instructions. Once completed, the Student_BM tab will serve as the basis for writing your management report. It is expected that any conclusions you draw in the management report will be consistent with the data and analyses contained in the spreadsheet. Instructions Data Analysis Component: 1. Make a pivot table with:

Business

Student (Rows),

Athlete

(Rows),

Cheated

(Columns), and Cheated (Summed Value). 2. Create a bar chart showing cheating by athletes and business students. 4. Determine if there is a statistical difference between nonathlete BDT business students and the national average for business students

as reported by the Chronicle of Higher Education. 5. Determine if there is a statistical difference between athlete BDT business students and the national average for business students as reported by the Chronicle of Higher Education. 6. Determine if there is a statistical difference between BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
7. Determine if there is a statistical difference between BDT nonbusiness students and the national average for nonbusiness

students as reported by the Chronicle of Higher Education.

Instructions Data Interpretation Component: Utilizing the data you have analyzed, write a managerial report of 500-800 words to the dean. The managerial report needs to include an introduction, analysis, conclusion, and a minimum of three supporting references. 1. Introduction (Define): Explain in your own words why you are providing this report and the problem(s) you are trying to solve. 2. Collect: Describe the data set you used. 3. Organize: Describe your pivot table. 4. Visualize: Include and describe your bar chart. 5. Analyze: Provide a summary of your conclusions based upon the four population proportion hypothesis tests. 6. The Dean has expressed a concern related to the amount of cheating currently taking place at Bo Diddley Tech and has strongly suggested that you “tweak” the statistical data such that it favors the image of the university. Discuss the potential use of unethical manipulation of statistical data to provide a biased outcome as well as the ethical counter proposal you would offer the dean in this scenario. 7. Conclusion: What advice would you give to the dean based on your analysis of the data?

Student_BM

Athlete Cheated

Business

Proportion Proportion Proportion

Sample Size (n)
=count(range) Sample Size (n)
=count(range) Sample Size (n)
=count(range)

Cheated Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated Response of Interest (ROI) Cheated

Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI) Count for Response (CFR)
=COUNTIF(range,ROI)

Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n Sample Proportion (pbar)
=CFR/n

Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po

Hypothesized 0.56 Hypothesized 0.56 Hypothesized

Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95 Confidence Coefficient (Coe) 0.95

Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05 Level of Significance (alpha)
=1-Coe 0.05

Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0! Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n) ERROR:#DIV/0!

ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0! Test Statistic (Z-stat)
=(pbar-Hypo)/StdError ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0! Accept or Reject: Left Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0! Accept or Reject: Right Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0! Accept or Reject: Two Tail ERROR:#DIV/0!

ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0! p-value (Lower Tail)
=NORM.S.DIST(z,TRUE) ERROR:#DIV/0!

ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0! p-value (Upper Tail)
=1-LowerTail ERROR:#DIV/0!

ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0! p-value (Two Tail)
=2*MIN(LowerTail,UpperTail) ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0! Accept or Reject p-value: Left Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0! Accept or Reject p-value: Right Tail ERROR:#DIV/0!

ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0! Accept or Reject p-value: Two Tail ERROR:#DIV/0!

ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0!

ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0! p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n) ERROR:#DIV/0!

College 1. Pivot Table Nationwide Average % Cheated
Insert pivot table in this cell – F2 56%
Nonbusiness 47%
2. Bar Chart
Bar chart starts in this cell – F20
Insert the appropriate numbers into the hypothesis testing calculations below based upon your pivot table results. Note the results.
3-6 Hypothesis Test
Business Nonathlete vs. National Average Business Athlete vs. National Average Business vs. National Average Nonbusiness vs. National Average
Proportion
Sample Size (n)
=count(range)
Response of Interest (ROI)
Count for Response (CFR)
=COUNTIF(range,ROI)
Sample Proportion (pbar)
=CFR/n
Highlight your H0 and Ha Two Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po Right Tail H0: p ≤ po Ha: p > po
Hypothesized 0.56 0.47
Confidence Coefficient (Coe) 0.95
Level of Significance (alpha)
=1-Coe		 0.05
Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n)		 ERROR:#DIV/0!
Test Statistic (Z-stat)
=(pbar-Hypo)/StdError
Accept or Reject: Left Tail
Accept or Reject: Right Tail
Accept or Reject: Two Tail
p-value (Lower Tail)
=NORM.S.DIST(z,TRUE)
p-value (Upper Tail)
=1-LowerTail
p-value (Two Tail)
=2*MIN(LowerTail,UpperTail)
Accept or Reject p-value: Left Tail
Accept or Reject p-value: Right Tail
Accept or Reject p-value: Two Tail
p-Lower Limit
=pbar-CONFIDENCE.NORM(alpha,StdError,n)
p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n)

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