Unit Sales Price Personal Advertising Selling Expenses 132 $74 $1,140 203 74 1,400 217 55 1,160 255 53 1,210 252 64 1,490 239 70 1,460 152 75 1,200 197 58 1,020 230 65 1,390 154 61 1,040 As a first step in the analysis, the company ran simple regressions of unit sales on each of the potentially important independent variables of price and personal selling expenses: The first simple regression equation is: SALES = 371 ? 2.59 PRICE Predictor Coef Stdev t ratio p Constant 371.0 109.5 3.39 0.010 PRICE ?2.587 1.676 ?1.54 0.161 SEE = 40.94 R2 = 22.9% = 13.3% The second simple regression equation is: SALES = 5.9 + 0.158 SELLEXP Predictor Coef Stdev t ratio p Constant 5.89 90.10 0.07 0.949 SELLEXP 0.15764 0.07142 2.21 0.058 SEE = 36.77 R2 = 37.8% = 30.1% The multiple regression equation is: SALES = 195 ? 4.33 PRICE + 0.231 SELLEXP Predictor Coef Stdev t ratio p Constant 194.92 38.27 5.09 0.000 PRICE ?4.3296 0.5396 ?8.02 0.000 SELLEXP 0.23115 0.02560 9.03 0.000 SEE = 12.31 R2 = 93.9% = 92.2% A. Based on these simple regression model results, do either of the potentially important independent variables affect unit sales? B. Characterize the differences between each simple regression model coefficient estimate from part A with those estimated using the following multiple regression: C. What other variables would be important to sell the machines?