Question
Developing multiple regression model and interpreting The district sales manager for a major automobile manufacturer is studying car sales. Specifically, he would like to determine
Developing multiple regression model and interpreting
The district sales manager for a major automobile manufacturer is studying car sales. Specifically, he would like to determine what factors affect the number of cars sold at a dealership. To investigate, he randomly selects 14 dealers and obtains data on the number of cars sold last quarter, the minutes of radio advertising purchased last quarter, the number of full-time salespeople employed in the dealership, and whether or not the dealer is located in the city (1 if city, 0 if not). The data are as follows:
No. of Cars sold, Y | Advertising X1 | Sales Force X2 | Location X3 | No. of Cars sold, Y | Advertising X1 | Sales Force X2 | Location X3 |
150 127 138 159 144 139 128 | 20 18 15 22 23 17 16 | 11 10 15 14 13 12 12 | 1 0 1 1 0 1 0 | 170 161 180 102 163 106 149 | 30 25 26 15 24 18 25 | 15 14 17 7 16 10 11 | 1 1 0 1 1 0 1 |
- Develop a correlation matrix. Which independent variable has the strongest correlation with the dependent variable? Is there any multicollinearity problem?
- Determine the regression equation. How many cars would the sales manager expect to be sold by a dealership employing 20 salespeople, purchasing 20 minutes of advertising, and located in the city.
Interpret the regression outputs specifically, the Adjusted R2, standard error of estimate, the F test, the regression coefficients and the test of significance.
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