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The ANOVA Procedure |

The ANOVA procedure is one of several procedures available in SAS/STAT software for analysis of variance. The ANOVA procedure is designed to handle balanced data (that is, data with equal numbers of observations for every combination of the classification factors), whereas the GLM procedure can analyze both balanced and unbalanced data. Because PROC ANOVA takes into account the special structure of a balanced design, it is faster and uses less storage than PROC GLM for balanced data.

Use PROC ANOVA for the analysis of balanced data only, with
the following exceptions:
one-way analysis of variance,
Latin square designs, certain partially balanced incomplete
block designs, completely nested (hierarchical) designs, and
designs with cell frequencies that are proportional to each other
and are also proportional to the background population.
These exceptions have designs in which the factors are all
orthogonal to each other.
For further discussion, refer to Searle (1971, p. 138).
PROC ANOVA works for designs with block diagonal **X**'**X**
matrices where the elements of each block all have the same
value. The procedure partially tests this requirement
by checking for equal cell means. However, this test is imperfect:
some designs that cannot be analyzed correctly may pass the test, and
designs that can be analyzed correctly may not pass.
If your design does not pass the test, PROC ANOVA produces a
warning message to tell you that the design is unbalanced and
that the ANOVA analyses may not be valid;
if your design is not one of the special cases described here,
then you should use PROC GLM instead.
Complete validation of designs is not performed in PROC ANOVA
since this would require the whole **X**'**X** matrix; if
you're unsure about the validity of PROC ANOVA for your design,
you should use PROC GLM.

**Caution:** If you use PROC ANOVA for analysis of unbalanced data, you
must assume responsibility for the validity of the results.

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