Benchmark - Ethical Behavior of Business Students at Bo Diddley Tech

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BUS-352-RS-Benchmark-BoDiddleyTechDataset.xlsx
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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

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.
3-6 Hypothesis Test
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)
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)
Sample Proportion (pbar) =CFR/n 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 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
Confidence Coefficient (Coe) 0.95 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 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! 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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!