Question

The sales manager of a large automotive parts distributor wants to develop a model to forecast as early as May the total annual sales of a region. If regional sales can be forecast, then the total sales for the company can be forecast. The number of retail outlets in the region stocking the companys parts and the number of automobiles registered for each region as of May 1 are the two the independent variables investigated. The data appear in Table P-12 How much error is involved in prediction for region 1? Forecast the annual sales for region 12, given 2,500 retail outlets and 20.2 millions automobiles registered. Provide both point forecast and the approximate 95% confidence interval for your point forecast. Discuss the accuracy of the forecast. Give an interpretation of the regression coefficients. Are these regression coefficients sensible? How can the regression equation can be improved? The sales manager decides to investigate a new independent variable, personal income by region. The data for this new variable are presented in Table P-13. Does personal income by region make a contribution to forecasting of sales? 1. a. b. c. d. e. f. Forecast annual sales for region 12 for personal income of $40 billion and the values for retail outlets and automobiles registered given in part b. Discuss the accuracy of the forecast made Which independent variables would you include in your final forecast model? Why? 8.
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Answer #1

a) use minitab to obtain the correlation matrix for the given data

MINITAB procedure :
step 1: selet star > basic statistics > correlation .
step 2: in variables, select sales, outlets, and Auto from the box on the left.
step 3: click OK in all boxes.

MINITAB output :

Correlations: Sales, Outlets, Auto sales Outlets 0.739 0.009 Outlets 0.548 0.081 0.670 0.024 Auto cell contents: Pearson correlation P-Value

the correlation value for outlet is 0.739 and auto is 0.58 the correlation value of the independent variables 0.670.

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