Basic Analysis and Graphing - SAS

160 Performing Oneway Analysis Chapter 5
Normal Quantile Plot
Table 5.20 Description of the Power Details Report (Continued)
Number (n)
Solve for Power
Solve for Least
Significant Number
Solve for Least
Significant Value
Adjusted Power and
Confidence Interval
Total sample size across all groups. Initially, the actual sample size is put in
the first position.
Solves for the power (the probability of a significant result) as a function of
all four values: α, σ, δ, and n.
Solves for the number of observations needed to achieve approximately 50%
power given α, σ, and δ.
Solves for the value of the parameter or linear test that produces a p-value of
α. This is a function of α, σ, n, and the standard error of the estimate. This
feature is available only when the X factor has two levels and is usually used
for individual parameters.
When you look at power retrospectively, you use estimates of the standard
error and the test parameters.
• Adjusted power is the power calculated from a more unbiased estimate of
the non-centrality parameter.
• The confidence interval for the adjusted power is based on the
confidence interval for the non-centrality estimate.
Adjusted power and confidence limits are computed only for the original
Delta, because that is where the random variation is.
Related Information
• “Example of the Power Option” on page 172
• “Statistical Details for Power” on page 180
Normal Quantile Plot
You can create two types of normal quantile plots:
• Plot Actual by Quantile creates a plot of the response values versus the normal quantile values. The
quantiles are computed and plotted separately for each level of the X variable.
• Plot Quantile by Actual creates a plot of the normal quantile values versus the response values. The
quantiles are computed and plotted separately for each level of the X variable.
The Line of Fit option shows or hides the lines of fit on the quantile plots.
Related Information
• “Example of a Normal Quantile Plot” on page 174