MARK 239 INFORMATION FOR MARKETING DECISIONS TUTORIAL ASSIGNMENT #3: MULTIPLE regression analysis BACKGROUND A food products manufacturing company has developed a new low-priced high-energy bar for runners, mountain climbers and other people involved in prolonged strenuous activities. The market for such products has grown rapidly over the past few years, and the company believes that there is a potentially strong market for the energy bar. Before the bar is launched nationally, the company ” is Marketing Manager is given the task of running a test market trial and in particular to determine the effect that price and in-store promotions (e. g. signs, displays, coupons and free samples) will have on sales.
The data so obtained will enable the company to develop an effective launch strategy for the new product. A random sample of 34 retail stores is selected for the trial. Price is varied across the stores (i. e. 12 will sell the bar for 59 cents, 12 will sell it at 79 cents and 10 for 99 cents), as is the monthly promotion expenditure. Data is collected on the number of bars sold in the trial month (the dependent variable).
The data collected during the trial were as shown in the following table. METHOD Follow the same procedure as in last week ” is tutorial. Enter the data, creating three variables. Run a multiple regression analysis with this data, testing for any outliers and for the assumptions of col linearity, normality, linearity and homoscedasticity. Answer the following questions, and include the relevant items from your analysis output in the submitted assignment. N.
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B. You can include the appropriate items from the output (i. e. tables and graphs) by cutting and pasting them between SPSS and Word using the following procedure: (a) in the SPSS Viewer, click on the output item to select it and then in the Edit menu choose Copy objects; (b) now in the Word document put the cursor at the insertion point then select Edit and Paste. QUESTIONS 1. Are there any outliers in the data? In one sentence, explain how you determined this from the output.
(1 mark) 2. Are the assumptions of normality, linearity and homoscedasticity met by this data? In one sentence, explain how you determined this from the output. (1 mark) 3. How would you assess the model in terms of the variance explained and statistical significance? (2 marks) 4. What are the contributions of each independent variable to the prediction of the dependent variable in the model? (2 marks) 5. Specify the multiple regression equation for the model.
Explain what each of the coefficients in this equation means. (3 marks) 6. If the price of the bar was 65 cents and the promotion budget was $500, what would you predict the sales level to be? (1 mark).
