Modeling and Multivariate Methods - SAS

106 Fitting Standard Least Squares Models Chapter 3
Effect Options
Table 3.10 Description of the Save Columns Options (Continued)
Mean Confidence Limit
Formula
Indiv Confidence Limit
Formula
Save Coding Table
Creates a new column in the data table containing a formula for the mean
confidence intervals.
Note: If you hold down the SHIFT key and select the option, you are
prompted to enter an α-level for the computations.
creates a new column in the data table containing a formula for the
individual confidence intervals.
Note: If you hold down the SHIFT key and select the option, you are
prompted to enter an α-level for the computations.
Produces a new data table showing the intercept, all continuous terms, and
coded values for nominal terms.
Note: If you are using the Graph option to invoke the Profiler, then you should first save the columns
Prediction Formula and StdErr Pred Formula to the data table. Then, place both of these formulas into the
Y,Prediction Formula role in the Profiler launch window. The resulting window asks whether you want to
use the PredSE colname to make confidence intervals for the Pred Formula colname, instead of making a
separate profiler plot for the PredSE colname.
Effect Options
Use the red triangle options next to an effect to request reports, plots, and tests for the effect. You can close
results or dismiss the results by deselecting the item in the menu.
Table 3.11 Description of Effect Options
LSMeans Table
LSMeans Plot
LSMeans Contrast
LSMeans Student’s t
Shows the statistics that are compared when effects are tested. See “LSMeans
Table” on page 107.
Plots least squares means for nominal and ordinal main effects and for
interactions. See “LSMeans Plot” on page 109.
Note: This option is enabled only for pure classification effects.
Specify contrasts for pure classification effects to compare levels. See
“LSMeans Contrast” on page 110.
Computes individual pairwise comparisons of least squares means in the
model using Student’s t-tests. This test is sized for individual comparisons. If
you make many pairwise tests, there is no protection across the inferences.
Thus, the alpha-size (Type I) error rate across the hypothesis tests is higher
than that for individual tests.
See the Basic Analysis and Graphing book.