5. (15 pis.) Tools and Robots wants to forecast their RoboLaundry sales. Lucy Mendoza, the company manage believes that its sales are directly related to the number of new housing units in the area and the promotion budget. She gathered data from the locality's records on monthly house construction permits, the month! promotions budget and the monthly sales from the store records. She did regression using the identified factor against the sales. The following are the excel reports on the two models she considered. + Regression Statistics (Model 1) Multiple R 0.70 R Square 0.49 Adjusted R Square 0.43 Standard Error 3.11 Observations 10 ANOVA 55 MS F SAG F Regression 1 74.13 74.13 7.65 0.02 Residual (a) ( c) (e) Total (b ) (d) Lower Upper Srd Error I Stat value 95N 95% Intercept 2.36 (f) 1.07 0.31 2.71 7.43 Monthly Construction Permits 0.27 0.0 0.04 0.49 Regression Stathtics (Model 2) Multiple R 0.76 R Square 0.58 Adjusted R Square 0.46 Standard Error 3.01 Observations 10 ANOVA MS Significance F Regression 0.05 Residual Total Couff Error Lower 9516 Intercept 5.30 -20.66 10.06 Monthly Promotions budget 0.46 -0.41 Monthly Construction Permits (in thousands] 0.31 0.08 0.55 (a) Compute the missing entries (a) to (f) in the regression output for model 1. (b) What is the forecast equation for model 1? Using this equation, forecast RoboLaundry sales if 30 construction permits for new homes are filed. (c) What is the interpretation of the coefficient , for model 1? (d) What is , and what does it imply for model 1? (e) The adjusted / increased from model I to model 2, does it immediately imply that model 2 is a better model than model 1? Why/why not? (f) Lucy Mendoza is considering adding another factor corresponding to the seasons in a year (winter, spring, summer, and fall), explain how she will include this in her model