Week 4 Case Study – excel

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You manage human relations for your company. One of your sales managers has retired, leaving an opening in the organization. You are considering two different employees for the position. Both are highly qualified, so you have decided to evaluate their sales performance for the past year.

Using the attached spreadsheet create and calculate the following in Microsoft® Excel®:

  1. Determine the range of values in which you would expect to find the average weekly sales for the entire sales force in your company 90% of the time, and calculate the following:
    • The impact of increasing the confidence level to 95%
    • The impact of increasing the sample size to 150, assuming the same mean and standard deviation, but allowing the confidence level to remain at 90%
  2. Based on the calculated confidence interval for weekly sales on the sample of 50 reps at a 90% confidence level, calculate the following:
    • Both reps’ average weekly performance, highlighting if it is greater than the population mean
  3. In order to decide who to promote, determine whether there is a statistically different average weekly sales between Sales Rep A and Sales Rep B by doing the following:
    • Create null and alternative hypothesis statements that would allow you to determine whether their sales performance is statistically different or not.
    • Use a significance level of .05 to conduct a t-test of independent samples to compare the average weekly sales of the two candidates.
    • Calculate the p-value.
  4. Considering the individual you did not promote, do the following:
    • Determine whether this person’s average weekly sales are greater than the average weekly sales for the 50 sales reps whose data you used to develop confidence intervals.
    • Create null and alternative hypothesis statements that would allow you to determine whether this person’s weekly average sales are greater than the sample of Sales Reps.
    • Use a significance level of .05 to conduct a t-test of independent samples to compare the average weekly sales of both.
    • Calculate the p-value.
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