Evaluate regression output to determine most appropriate model. E3.12 (LO 3), AP Ning is trying to budget her company's sales for the upcoming year using a model based on past data. She believes the company's marketing expenditures should help explain sales, as should the number of units sold and the number of salespeople working. After gathering data for all of these variables, she generated a single regression model for each possible cost driver, as follows. Marketing Expenditures R-Square 0.5968 Coefficients Standard Error T-stat P-value Intercept 212766.667 36071.22353 5.89851538 0.00015136 Marketing expenditures 2.06 0.535491467 3.84693338 0.00322834 Number of Units Sold R-Square 0.3601 Coefficients Standard Error T-stat P-value Intercept 214489.633 56980.68389 3.76425165 0.00369626 Number of units sold 90.3774588 38.09840547 2.37221106 0.03912928 Number of Salespeople R-Square 0.0111 Coefficients Standard Error T-stat P-value Intercept 328004.202 57654.57665 5.68912688 0.0002013 Number of salespeople 2731.09244 8153.588423 0.33495589 0.74457844 Required Are any of these possible single regression models valid? Explain. After evaluating the single regression models, Ning generated three more models, each including two of the three possible cost drivers. Select output for each of these models