PROC ANOVA first displays a table that includes the following:
- the name of each variable in the CLASS statement
- the number of different values or Levels of the Class variables
- the Values of the Class variables
- the Number of observations in the data set and the
number of observations excluded from the analysis
because of missing values, if any
PROC ANOVA then displays an analysis-of-variance table
for each dependent variable in the MODEL statement.
This table breaks down
- the Total Sum of Squares for the dependent variable
into the portion attributed to the Model
and the portion attributed to Error
- the Mean Square term, which is
the Sum of Squares divided by
the degrees of freedom (DF)
The analysis-of-variance table also lists the following:
- the Mean Square for Error (MSE), which is an estimate
of , the variance of the true errors
- the F Value, which is the ratio produced by dividing the
Mean Square for the Model by the Mean Square for Error.
It tests how well the model as a whole (adjusted for the
mean) accounts for the dependent variable's behavior.
This F test is a test of the null hypothesis that
all parameters except the intercept are zero.
- the significance probability associated
with the F statistic, labeled "Pr > F"
- R-Square, R2, which measures how much variation in the
dependent variable can be accounted for by the model.
The R2 statistic, which can range from 0 to 1, is the ratio
of the sum of squares for the model divided by
the sum of squares for the corrected total.
In general, the larger the R2 value,
the better the model fits the data.
- C.V., the coefficient of variation, which is often used
to describe the amount of variation in the population.
The C.V. is 100 times the standard deviation
of the dependent variable divided by the Mean.
The coefficient of variation is often a
preferred measure because it is unitless.
- Root MSE, which estimates the standard
deviation of the dependent variable.
Root MSE is computed as the square root of Mean
Square for Error, the mean square of the error term.
- the Mean of the dependent variable
For each effect (or source of variation) in the model,
PROC ANOVA then displays the following:
- DF, degrees of freedom
- Anova SS, the sum of squares, and the associated Mean Square
- the F Value for testing the hypothesis that
the group means for that effect are equal
- Pr > F, the significance probability
value associated with the F Value
When you specify a TEST statement, PROC ANOVA displays the
results of the requested tests.
When you specify a MANOVA statement and the model includes more
than one dependent variable, PROC ANOVA produces these
- the characteristic roots and vectors of
E-1H for each H matrix
- the Hotelling-Lawley trace
- Pillai's trace
- Wilks' criterion
- Roy's maximum root criterion
See Example 30.6 in Chapter 30, "The GLM Procedure," for
an example of the MANOVA results.
These MANOVA tests are discussed in Chapter 3, "Introduction to Regression Procedures."
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.