## Type I Tests

Type I tests examine the sequential incremental improvement
in the fit of the model as each effect is added.
They can be computed by fitting the model
in steps and recording the difference in
error sum of squares (linear models) and
log-likelihood statistics (generalized linear models).
The **Type I Tests** table for linear models, as illustrated
by Figure 39.13, includes the following:
**Source**
- is the name for each effect.

**DF**
- is the degrees of freedom associated with each effect.

**Sum of Squares**
- is the incremental error sum of squares for the model
as each effect is added.

**Mean Square**
- is the sum of squares divided by its associated
degrees of freedom.

**F Stat**
- is the
*F* statistic for testing the null hypothesis
that the parameters for the added effect are 0.
This is formed by dividing the mean square for the effect
by the mean square for error from the complete model.
**Pr > F**
- is the probability of obtaining a greater
*F* statistic
than that observed if the null hypothesis is true.

**Figure 39.13:** Type I Tests Table

The **Type I (LR) Tests** table for generalized
linear models, as illustrated by Figure 39.14,
includes the following:
**Source**
- is the name for each effect.

**DF**
- is the degrees of freedom associated with each effect.

**ChiSq**
- is the value for testing the null
hypothesis that the parameters for the added effect are 0.
This is evaluated as twice the incremental log-likelihood
for the model as each effect is added, and it has an asymptotic
distribution under the null hypothesis.
**Pr > ChiSq**
- is the probability of obtaining a greater statistic than that observed, if the null hypothesis is true.

**Figure 39.14:** Type I Likelihood Ratio Tests

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.