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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.
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Coefficients |