Basic Analysis and Graphing - SAS

102 Performing Bivariate Analysis Chapter 4
Fit Line and Fit Polynomial
Table 4.5 Description of the Lack of Fit Report (Continued)
DF
Sum of Squares
Mean Square
F Ratio
Prob > F
Max RSq
The degrees of freedom (DF) for each source of error.
• The Total Error DF is the degrees of freedom found on the Error line of the
Analysis of Variance table (shown under the “Analysis of Variance Report” on
page 103). It is the difference between the Total DF and the Model DF found in
that table. The Error DF is partitioned into degrees of freedom for lack of fit and
for pure error.
• The Pure Error DF is pooled from each group where there are multiple rows
with the same values for each effect. See “Statistical Details for the Lack of Fit
Report” on page 125.
• The Lack of Fit DF is the difference between the Total Error and Pure Error DF.
The sum of squares (SS for short) for each source of error.
• The Total Error SS is the sum of squares found on the Error line of the
corresponding Analysis of Variance table, shown under “Analysis of Variance
Report” on page 103.
• The Pure Error SS is pooled from each group where there are multiple rows with
the same value for the x variable. This estimates the portion of the true random
error that is not explained by model x effect. See “Statistical Details for the Lack
of Fit Report” on page 125.
• The Lack of Fit SS is the difference between the Total Error and Pure Error sum
of squares. If the lack of fit SS is large, the model might not be appropriate for
the data. The F-ratio described below tests whether the variation due to lack of
fit is small enough to be accepted as a negligible portion of the pure error.
The sum of squares divided by its associated degrees of freedom. This computation
converts the sum of squares to an average (mean square). F-ratios for statistical tests
are the ratios of mean squares.
The ratio of mean square for lack of fit to mean square for Pure Error. It tests the
hypothesis that the lack of fit error is zero.
The probability of obtaining a greater F-value by chance alone if the variation due to
lack of fit variance and the pure error variance are the same. A high p value means
that there is not a significant lack of fit.
The maximum R 2 that can be achieved by a model using only the variables in the
model.
See “Statistical Details for the Lack of Fit Report” on page 125.