A householdappliance manufacturer wants to analyze the relationship between total sales and the company's three primary means of advertising, by type, for each of 10 randomly selected sales periods. All data are in millions of dollars. A multiple regression analysis on the data using the variables television, magazine, and radio advertising expenditures as predictor variables for sales was performed. Complete parts a) though o) below. Click the icon to view the computer output. Ii Click the icon to view the scatterplot matrix, a. Use the scatterplot matrix to assess whether a multiple linear regression model might be appropriate for predicting sales. Choose the correct answer below. 0 A. A multiple linear regression model appears to be appropriate because the predictor variables seem to have a linear relationship with one another 0 B. A multiple linear regression model appears to be appropriate because sales seems to have a linear relationship with each of the predictor variables. 0 C. A multiple linear regression model does not appear to be appropriate because the predictor variables do not seem to have a linear relationship with one another. 0 D. A multiple linear regression model does not appear to be appropriate because sales does not seem to have a linear relationship with each of the predictor variables. b. Use the computer output to obtain the sample regression equation for sales in terms of television, magazine, and radio advertising expenditures. SALES = + TV + MAG + RADIO (Type integers or decimals.) c. Apply the sample regression equation to predict total sales if the amounts spent on television, magazine, and radio advertising are $9.6 million, $4.4 million, and $5.1 million, respectively. The predicted total sales is 3 million. (Round to two decimal places as needed.) Computer Output The regression equation is SALES = 266 + 6.73TV + 3.26MAG + 4.51RADIO Predictor Coef SE Coef T P Constant 266.23 16.34 16.29 0.000 TV 6.727 1.344 5.01 0.002 MAG 3.257 1.642 1.98 0.095 RADIO 4.507 3.703 1.22 0.269 S = 4.418 R-SQ = 91.1% R-SQ(adj) = 86.6% Analysis of Variance Source DF SS MS F P Regression 3 1194.53 398.18 20.40 0.002 Residual Error 6 117.11 19.52 Total 9 1311.64