A marketing manager has developed a regression model to predict quarterly sales of his company's mid-weight microber jackets based on price and amount spent on advertising. An intern suggests that he include indicator (dummy) variables for each quarter. Complete parts (a) through (c) below. a) How would you code the variables? (How many dummy variables do you need? What values would they have?) (:3 A. There should be 1 indicator variable for one season. The season chosen does not matter, but it should have a value of 1 for that season and 0 for the others. (:3 B. There should be 4 variables. The variables could be spring (1 for spring, 0 for others), summer (1 for summer, 0 for others), fall (1 for fall, 0 for others), and winter (1 for winter, 0 for others). . There should be 1 variable. The values could be 0 for spring, 1 for summer, 2 for fall, and 3 for winter. . There should be 3 variables. The variables could be spring (1 for spring, 0 for others), summer (1 for summer, 0 for others), and fall (1 for fall, 0 for others). b) Why does the intern's suggestion make sense? . The amount spent on advertising probably changes with the season. Indicator variables can model this well. . The jacket prices probably change with the season. Indicator variables can model this well. . Sales of jackets probably have a seasonal component. Indicator variables can model this well. . The suggestion does not make sense. The variation with season would be captured by the linear regression model already. c) Do you think a regression with the indicator variables would model jacket sales better than one without those predictors? {"3 A. Yes, because sales are likely seasonal, and indicator variables for the seasons can model this behavior well. 'i B. Yes, because adding any term to a regression model will always make a signicant increase in the accuracy of the model. A C. No, because the original model has enough variables to accurately capture the change in sales with the seasons. D. No, because adding more than one indicator variable for different seasons will result in collinear independent variables, which can cause problems with the model