[ Data Screening || Matrix Algebra with SAS/IML || Regression analysis || General Linear Models || Canonical correlation, Discriminant analysis || Logistic Regression || Factor Analysis || Clustering and scaling || SAS macro programs || Data sets || Programs]

## Regression examples

These tutorial examples use a few different data sets to illustrate a variety of methods involved in regression analysis.
autocox.sas
Box-Cox transformation for the Auto data. Uses the BOXCOX macro to find a power transformation of MPG.
basecox.sas
Box-Cox transformation for the Baseball data. Uses the BOXCOX macro to find a power transformation of Salary.
bisqdunc.sas
Robust Regression - Duncan data. Uses the BISQUARE macro.
bisqfuel.sas
Robust Regression - Fuel data. Uses the BISQUARE macro.
bloodreg.sas
Testing homogeneity of regression. Compares models with different slopes and intercepts in the BLOOD data.
bootdunc.sas
Bootstrap Regression - Duncan data. Uses the BOOT macro.
crossval.sas
Plot of predictive R-square, showing effects of shrinkage.
detroit1.sas
Predicting Detroit homicide rates. See the description of DETROIT.SAS under Data sets.
detroit2.sas
Stepwise selection methods on Detroit Homicide data.
evappca.sas
Incomplete principal components regression.
fitbipl.sas
Fitness data: Biplot.
fitcp.sas
Fitness Data: CP Plot.
fitcolin.sas
Fitness Data: Collinearity diagnostics.
fitinfl.sas
Fitness Data: Influence plot, bubble proportional to Cook's Distance
fitness2.sas
Influence diagnostics and partial residual plots
fitness3.sas
Various selection methods for Fitness data
fitness4.sas
Cross validation of a regression model. We hold back a portion of the data from the fit, and evaluate how well the model predicts in the hold-back sample.
fitpart.sas
Fitness data: Partial residual plots.
fitstep.sas
Illustrate stepwise selection using the Fitness Data.
fuelcox.sas
Fuel Consumption: Box Cox Transformation Plots.
fuelcp.sas
C(p) and related plots for fuel data.
fuelerr.sas
Fuel Consumption: Demonstrates effects of errors-in-predictors.
fuelinfl.sas
Fuel Consumption: Influence Plot. Uses the INFLPLOT macro to show the influence of a few observations on prediction of fuel consumption.
fuelpart.sas
Fuel data: Partial residual plots
fuelr1.sas
Fuel data: Building a regression model
health.sas
Suppression effects: Health, Height and Weight
mregppvt.sas
Multivariate multiple regression
regdemo.sas
Regression example, using SAS/IML. A set of SAS/IML modules to carry out regression computations and hypothesis tests. Duplicates PROC REG facilities, while the IML program shows how it's done.
reginfl.sas
Influential Observations in Multiple Regression. Fictitious data to illustrate various combinations of residual and leverage to produce influential observations.
robdunc.sas
Robust Regression - Duncan data, using the ROBUST macro.
robfuel.sas
Bisquare Robust Regression - Fuel data, using the ROBUST macro.
robreg.sas
Robust Regression using IRLS(via PROC NLIN; superceded by ROBUST macro).
scatfuel.sas
Fuel data: scatterplot matrix
stepsim.sas
Stepwise regression simulation example NO real predictors. The maxim if you torture the data long enough, you can make it confess to anything is illustrated by constructing 100 random predictors, all statistically independent of the response. How many do you suppose will be significant at the .05 level?
stepsim2.sas
Stepwise simulation experiment: add random predictors to Fitness Data. What do you think happens if you throw a whole bunch of random predictors into stepwise regression with a real data set?
suppress.sas
Demonstrate suppression effect. Fictitious data on three variables are used to illustrate a situation in which an additional variable can make a greater extra contribution to regression, SSR(X2|X1), than it does by itself, SSR(X2).
suppres2.sas
Partial residual plots for supression example
therboot.sas
Simple Regression: Bootstrap samples and estimates
turnip.sas
Lurking variables: Turnip Green Data. Plots of results from a model to predict the quantity of Vitamin B2 in turnip greens reveal a surprising and unsuspected lurking variable.