coression Statistics 0.9035 RSquare Adjuster Square Standard Error 1.7353 Observations ANOVA SS MS Significance 8.01125-07 Regression Residual 327 85 Er Star P-value Lower 455 Upper 959 EXD Salary EDI Coefficients Standar 1.6.152 0.2393 0.1254 0.0127 0.0016 2.9845 1.3553 0.0709 0.0097 0.0396 0.0205 1070 or this is present in the regression model when independent 0./das variables within the model are highly correlated. 0.8466 O Significance 0.7502 O Multicollinearity 0.6439 Correlation Staya Homoscedasticity Net 309 Ford would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience (Exp), the employee's weekly base salary before commissions (Salary), and the education level of the employee. There are three levels of education in the sales force-high school degree, associate's degree, and bachelor's degree. The following dummy variables have been defined. Degree EDI ED2 High School 10 0 Associate's 1 mult Bachelor's of Letermination or this medis Expert An D The following Excel output shows the partially completed un regression output from a random sample of employees from the previous month 08430 Slay ahead in 07302 BULARY OUTPUT 0.5439 wurdles 000 Featured Intro to Statistic 30 questions L-a cpp 1 . 23 191796