Modeling and Multivariate Methods - SAS

64 Fitting Standard Least Squares Models Chapter 3
Estimates
Table 3.7 Description of the Lack of Fit Report (Continued)
Mean Square
F Ratio
Prob > F
Max RSq
Shows the mean square for the source, which is the SS divided by the DF.
Shows the ratio of the Mean Square for the Lack of Fit to the Mean Square
for Pure Error. It tests the hypothesis that the lack of fit error is zero.
Lists the p-value for the Lack of Fit test. A small p-value indicates a
significant lack of fit.
Lists the maximum R 2 that can be achieved by a model using only the
variables in the model. Because Pure Error is invariant to the form of the
model and is the minimum possible variance, Max RSq is calculated as
follows:
1 –
SS(Pure --------------------------------- Error)
SS(Total)
Estimates
Estimates options provide additional reports and tests for the model parameters.
Table 3.8 Description of Estimates Options
Show Prediction
Expression
Sorted Estimates
Expanded Estimates
Indicator
Parameterization
Estimates
Sequential Tests
Places the prediction expression in the report. See “Show Prediction
Expression” on page 65.
Produces a different version of the Parameter Estimates report that is more
useful in screening situations. See “Sorted Estimates” on page 65.
Use this option when there are categorical (nominal) terms in the model and
you want a full set of effect coefficients. See “Expanded Estimates” on
page 66.
Displays the estimates using the Indicator Variable parameterization. See
“Indicator Parameterization Estimates” on page 68.
Show the reduction in residual sum of squares as each effect is entered into
the fit. See “Sequential Tests” on page 68.
Custom Test Enables you to test a custom hypothesis. See “Custom Test” on page 68.
Joint Factor Tests
For each main effect in the model, JMP produces a joint test on all of the
parameters involving that main effect. See “Joint Factor Tests” on page 70.
Inverse Prediction Finds the value of x for a given y. See “Inverse Prediction” on page 70.