Even though the model fit looks positive, the first section of the coefficients table shows that there are too many predictors in the model. There are several non-significant coefficients, indicating that these variables do not contribute much to the model. To determine the relative importance of the significant predictors, look at the standardized coefficients. Even though Price in thousands has a small coefficient compared to Vehicle type, Price in thousands actually contributes more to the model because it has a larger absolute standardized coefficient. The second section of the coefficients table shows that there might be a problem with multicollinearity. For most predictors, the values of the partial and part correlations drop sharply from the zero-order correlation. This means, for example, that much of the variance in sales that is explained by price is also explained by other variables. The tolerance is the percentage of the variance in a given predictor that cannot be explained by the other predictors. Thus, the small tolerances show that 70%-90% of the variance in a given predictor can be explained by the other predictors. When the tolerances are close to 0, there is high multicollinearity and the standard error of the regression coefficients will be inflated. A variance inflation factor greater than 2 is usually considered problematic, and the smallest VIF in the table is 3.193.