Modeling and Multivariate Methods - SAS

Chapter 3 Fitting Standard Least Squares Models 107
Effect Options
Table 3.11 Description of Effect Options (Continued)
LSMeans Tukey HSD
LSMeans Dunnett
Test Slices
Power Analysis
Shows a test that is sized for all differences among the least squares means.
This is the Tukey or Tukey-Kramer HSD (Honestly Significant Difference)
test. (Tukey 1953, Kramer 1956). This test is an exact alpha-level test if the
sample sizes are the same and conservative if the sample sizes are different
(Hayter 1984).
See the Basic Analysis and Graphing book.
Performs multiple comparisons of each level against a control level. This test
is called Dunnett’s test. In the Fit Model platform, Hsu’s factor analytical
approximation is used for the calculation of p-values and confidence
intervals. The test results are also plotted.
Note: This option is available only for interaction effects.
For each level of each classification column in the interaction, it makes
comparisons among all the levels of the other classification columns in the
interaction. For example, if an interaction is A*B*C, then there is a slice
called A=1, which tests all the B*C levels when A=1. There is another slice
called A=2, and so on, for all the levels of B, and C. This is a way to detect
the importance of levels inside an interaction.
Shows the Power Details report, which enables you to analyze the power for
the effect test. For details, see “Parameter Power” on page 75.
LSMeans Table
Least squares means are predicted values from the specified model across the levels of a categorical effect
where the other model factors are controlled by being set to neutral values. The neutral values are the sample
means (possibly weighted) for regressors with interval values, and the average coefficient over the levels for
unrelated nominal effects.
Least squares means are the values that let you see which levels produce higher or lower responses, holding
the other variables in the model constant. Least squares means are also called adjusted means or population
marginal means. Least squares means can differ from simple means when there are other effects in the
model.
Least squares means are the statistics that are compared when effects are tested. They might not reflect
typical real-world values of the response if the values of the factors do not reflect prevalent combinations of
values in the real world. Least squares means are useful as comparisons in experimental situations.
Example of a Least Squares Means Table
1. Open the Big Class.jmp sample data table.
2. Select Analyze > Fit Model.
3. Select height and click Y.