SALESCALL Inc. has thousands of salespeople throughout the country. A sample of 100 salespeople is selected, and data is collected on the following variables. 1. SALES (the number of sales made this week) 2. CALLS (the number of sales calls made this week) 3. TIME (the average time per call this week) 4. YEARS (years of experience in the call center) 5. TYPE (the type of training, either group training, online training of no training)
a. The average (mean) sales per week exceeds 41.5 per salesperson. b. The true population proportion of salespeople that received online training is less than 55%. c. The average (mean) number of calls made per week by salespeople that had no training is less than 145. d. The average (mean) time per call is greater than 15 minutes. 1. Using the sample data, perform the hypothesis test for each of the above situations in order to see if there is evidence to support your manager’s belief in each case A–D. In each case, use the five-step hypothesis testing procedure, with α = .05, and explain your conclusion in simple terms. Also, be sure to compute the p-value and interpret. 2. Follow this up with computing 95% confidence intervals for each of the variables described in A–D and again interpreting these intervals. 3. Write a report to your manager about the results, distilling down the results in a way that would be understandable to someone who does not know statistics. Clear explanations and interpretations are critical.
Using Minitab, perform the regression and correlation analysis for the data on SALES (Y) and CALLS (X) by answering the following questions. 1. Generate a scatterplot for SALES versus CALLS, including the graph of the best fit line. Interpret. 2. Determine the equation of the best fit line, which describes the relationship between SALES and CALLS. 3. Determine the coefficient of correlation. Interpret. 4. Determine the coefficient of determination. Interpret. 5. Test the utility of this regression model (use a two tail test with α =.05). Interpret your results, including the p-value. 6. Based on your findings in 1–5, what is your opinion about using CALLS to predict SALES? Explain. 7. Compute the 95% confidence interval for beta-1 (the population slope). Interpret this interval. 8. Using an interval, estimate the average weekly sales for weekly calls that are 150. Interpret this interval. 9. Using an interval, predict the weekly sales when weekly calls are 150. Interpret this interval. 10. What can we say about the weekly sales when weekly calls are 300? Explain your answer. - In an attempt to improve this model, we attempt to do a multiple regression model predicting SALES based on CALLS, TIME, and YEARS. 11. Using Minitab, run the multiple regression analysis using the variables CALLS, TIME, and YEARS to predict SALES. State the equation for this multiple regression model. 12. Perform the global test for utility (F-Test). Explain your conclusion. 13. Perform the t-test on each independent variable. Explain your conclusions, and clearly state how you should proceed. In particular, state which independent variables should we keep, and which should be discarded. 14. Is this multiple regression model better than the linear model that we generated in parts 1–10? Explain.
Excerpt from file: HypothesisTesting MATH533CourseProjectPartB Name Instructor Date Inthehypothesistesting,twotypehypothesesaremade,oneiscalledAlternate hypothesisandsecondisnullhypothesis.Whatwouldberesearched,itiscalledAlter hypothesisandinagainstofalternatehypothesiscalledNullhypothesis. Ans.1
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