Ice Cream Sales Model An owner of an ice cream stand collected 90 days worth of data from last summer. She is attempting to fit a model predicting daily ice cream sales to better operate the stand next summer. She considers the following model: Model 1: Sales;= Bo + B1*Hours;+ 32*HighTemp; + 3*(Hours/'HighTemp) + &; Here, Sales; is the sales in dollars on the ith day, Hours; is the number of hours the stand was open on the ith day, HighTemp; is the high temperature on the ith day, and Hours/ HighTemp denotes the interaction between hours and high temperature. She fits this model using linear regression. The regression output is as follows: Call: 1m(formula = Income ~ Hours + HighTemp + Hours * HighTemp) Residuals : Min 10 Median 30 Max -68. 110 -15.579 2.773 17. 245 51. 604 Coefficients : Estimate Std. Error t value Pr(>It|) (Intercept) 14.5877 108 . 1674 0. 135 0. 8930 Hours 12. 2728 14. 6225 0. 839 0. 4036 HighTemp 0. 7198 1. 2264 0. 587 0. 5588 Hours : HighTemp 0. 3364 0. 1650 2. 038 0. 0446 * Signif. codes : 0 ' ***' 0. 001 '** ' 0.01 '* ' 0. 05 ' . ' 0.1 ' ' 1 Residual standard error: 25.09 on 86 degrees of freedom Multiple R-squared: 0. 9095, Adjusted R-squared: 0.9063 F-statistic: 288 on 3 and 86 DF, p-value: