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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.
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Stepwise Coefficients |