The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
A previous article shows how to use the MODELAVERAGE statement in PROC GLMSELECT in SAS to perform a basic bootstrap analysis of the regression coefficients and fit statistics. A colleague asked whether PROC GLMSELECT can construct bootstrap confidence intervals for the predicted mean in a regression model, as described in
I've written many articles about bootstrapping in SAS, including several about bootstrapping in regression models. Many of the articles use a very general bootstrap method that can bootstrap almost any statistic that SAS can compute. The method uses PROC SURVEYSELECT to generate B bootstrap samples from the data, uses the
It has been more than a decade since SAS 9.3 changed the default ODS destination from the old LISTING destination to more modern destinations such as HTML. One of the advantages of modern output destinations is support for Unicode symbols, superscripts, subscripts, and for formatting text by using boldface, italics,
In ordinary least squares regression, there is an explicit formula for the confidence limit of the predicted mean. That is, for any observed value of the explanatory variables, you can create a 95% confidence interval (CI) for the predicted response. This formula assumes that the model is correctly specified and
A SAS programmer wanted to use PROC SGPLOT in SAS to visualize a regression model. The programmer wanted to visualize confidence limits for the predicted mean at certain values of the explanatory variable. This article shows two options for adding confidence limits to a scatter plot. You can use a
The acceptance-rejection method (sometimes called rejection sampling) is a method that enables you to generate a random sample from an arbitrary distribution by using only the probability density function (PDF). This is in contrast to the inverse CDF method, which uses the cumulative distribution function (CDF) to generate a random