To value the potential Industrial properties appropriately, Dick Nelson has hired you as industrial real estate consultant. You have collected a sample of 5 7 sales of Industrial real estate properties that were sold recently in LA. County and San Bernardino County that met the requirements as potential sites to buy. You performed a Multiple Linear Regression analysis using the Sale Price as Response and relevant predictor variables. Now it is your turn to summarize the printout to Dick Nelson. Write a detailed executive summary based on the model given below, use the topics to write your summary. J MP Printout Response Sale Price Whole Model Actual by Predicted Plot 7000000 6000000 5000000 4000000 Sale Price Actual 3000000 2000000 2000000 4000000 6000000 Sale Price Predicted RMSE=569272 RSq=0.86 PValue<.0001 effect summary source logworth pvalue building area in sqfeet exterior wall facilities_score ceiling height feet miles to freeway of fit rsquare adj root mean square error response observations sum wgts variance df f ratio squares model prob> F C. Total 56 1.1521e+14 <.0001 parameter estimates term estimate std error t ratio prob> It] VIF Intercept -3609151 1097606 .3.29 0.0018* Building Area in Sqfeet 87.481444 5.233713 16.71 <.0001 miles to freeway .2.37 ceiling height in feet exterior wall facilities_score data set sample size model the estimated regression equation explain b0 b1 b2... does bl make sense kpis r-sq what is practical meaning of standard error estimate s hypothesis testing for f-test and t-tests there sufficient evidence conclude that contributes information predicting y predictors are contributing suggestions better how increase else do you need>