The stepwise algorithm chooses price and size (in terms of the vehicle wheelbase) as predictors. Sales are negatively affected by price and positively affected by size; your conclusion is that cheaper, bigger cars sell well. Price was chosen first because it is the predictor that is most highly correlated with sales. The remaining predictors are then analyzed to determine which, if any, is the most suitable for inclusion at the next step. Beta In is the value of the standardized coefficient for the predictor if it is included next. All of the significance values are less than 0.05, so any of the remaining predictors would be adequate if included in the model. To choose the best variable to add to the model, look at the partial correlation, which is the linear correlation between the proposed predictor and the dependent variable after removing the effect of the current model. Thus, wheelbase is chosen next because it has the highest partial correlation. After adding wheelbase to the model, none of the remaining predictors are significant. However, vehicle type just barely misses the 0.05 cutoff, so you may want to add it manually in a future analysis to see how it changes the results. Engine size would have a larger beta coefficient if added to the model, but it's not as desirable as vehicle type. This is because engine size has a relatively low tolerance compared to vehicle type, indicating that it is more highly correlated with price and wheelbase.