1.The manufacturer of a light fixture believes that the dollars spent on advertising, the price of the fixture, and the number of retail stores selling the fixture in a particular month influence the light fixture sales. The manufacturer randomly selects 10 months and collects the data. The sales are in thousands of units per month, the advertising is given in hundreds of dollars per month, and the price is the unit retail price for the particular month. Using Excel, the following computer output is obtained.

SUMMARY OUTPUT Regression Statistics Multiple R 0.9862 R Square 0.9726 Adjusted R Square 0.9589 Standard Error 4.9461 Observations 10 ANOVA of SS MS F Significance F Regression 5211.6192 1737.2064 71.0123 0.0000 Residual 6 146.7808 24.4635 Total g 5358.4 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 37.2886 8.7914 4.2415 0.0054 15.7768 58.8003 Advertising 0.5069 0.4412 1.1491 0.2943 -0.5726 1.5864 Price -0.3034 0.0815 -3.7243 0.0098 -0.5028 -0.1041 # of Stores 3.9191 3.0961 1.2658 0.2525 -3.6569 11.4950 a. Write the least squares prediction equation. b. Interpret the regression coefficients for the variables Advertising. Price, and Store. c. Based on the multiple regression model given above, estimate the monthly light fixture sales and calculate the value of the residual, if the company spends $4000 on advertising, the price of the fixture is $60, and the fixture is being sold at 3 retail stores