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

1. Develop a correlation matrix. Which independent variable has the strongest correlation with the dependent variable? Is there any multicollinearity problem?

1. 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.