Program 9.1: Running a Multiple Regression Model
Program 9.2: Using the RSQUARE Selection Method to Execute All Possible Models
Program 9.3: Generating Plots of R-Square, Adjusted R-Square, and Cp
Program 9.4: Demonstrating Forward, Backward, and Stepwise Selection Methods
Program 9.5: Setting the SLENTRY Value to .15 Using the Forward Selection Method
Program 9.6: Forcing Variables into a Stepwise Model
Program 9.7: Creating Dummy Variables for Regression
Program 9.8: Running PROG REG with Dummy Variables for Gender and Region
Program 9.9: Using the VIF to Detect Collinearity
Program 9.10: Detecting Influential Observations in Multiple Regression
Program 10.1: Comparing Proportions Using Chi-Square
Program 10.2: Rearranging Rows and Columns in a Table and Computing Relative Risk
Program 10.3: Tables with Small Expected Frequencies–Fisher’s Exact Test
Program 10.4: Computing Chi-Square from Frequency Data
Program 10.5: A SAS Macro for Computing Chi-Square from Cell Frequencies
Program 10.6: Computing Kappa Coefficient of Agreement
Program 10.7: Demonstrating Two Tests for Trend
Program 10.8: Computing Chi-Square for a One-Way Table
Program 11.1: Logistic Regression with One Categorical Predictor Variable
Program 11.2: Logistic Regression with One Continuous Predictor Variable
Program 11.3: Using a Format to Create a Categorical Variable
Program 11.4: Using a Combination of Categorical and Continuous Variables
Program 11.5: Running a Logistic Model with Interactions
Program 12.1: Plotting the Distribution of Income
Program 12.2: Performing a Wilcoxon Rank-Sum Test (aka Mann-Whitney U Test)
Program 12.3: Requesting an Exact p-Value for a Wilcoxon Rank-Sum Test
Program 12.4: Performing a Wilcoxon Signed-Rank Test
Program 12.5: Performing a Kruskal-Wallis ANOVA
Program 12.6: Performing the Ansari-Bradley Test for Spread
Program 12.7: Demonstrating PROC RANK
Program 12.8: Replacing Values with Ranks and Running a t-Test
Program 12.9: Using PROC RANK to Create Groups
Program 13.1: Computing Sample Size for an Unpaired t-Test
Program 13.2: Computing the Power of a t-Test
Program 13.3: Computing the Power for an ANOVA Design
Program 13.4: Computing Sample Size for a Difference in Two Proportions
Program 14.1: Taking a Simple Random Sample
Program 14.2: Taking a Random Sample with Replacement
Program 14.3: Rerunning the Program without the OUTHITS Option
Program 14.4: Requesting Replicate Samples
Acknowledgments
A tremendous amount of work went into bringing this book to the bookshelf, and all that work wasn’t done by me alone. Several factors combined to make the review process and the final production of this book a challenge.
First and foremost, I would like to thank John West, my editor and friend, who was amazingly patient and calm, even when there were technical challenges to overcome.
Next, we enlisted the help of more reviewers than usual. Four of these reviewers read the book from cover to cover and made excellent suggestions for improvements and found some subtle and obscure errors. So, kudos to Gerry Hobbs, Catherine Truxillo, Jeff Smith, and Marc Huber.
Other reviewers read chapters, particularly those where they had a particular expertise. Sincere thanks to Rob Agnelli, Paul Grant, Sanjay Matange, David Schlotzhauer, Jim Seabolt, and Sue Walsh.
Since the decision was made to use HTML output instead of simple list output, considerable extra effort was required. The production team needed to “touch” approximately 161 image files so that they would look good both in print as well as on the various eBook devices. The people involved in this process were: Jennifer Dilley, designer; Candy Farrell, technical publishing specialist; Joan Celmer, copyeditor; and Mary Beth Steinbach, managing editor.
No book would be successful without having people to market it. Thanks to Aimee Rodriguez and Stacey Hamilton for this essential task.
Finally, I salute the artists who created the front and back covers of the book. Nice job Jennifer Dilley and Marchellina Waugh.
Ron Cody, Summer 2011
Chapter 1 An Introduction to SAS
Statistical Tasks Performed by SAS
Variable Types in SAS Data Sets
Temporary versus Permanent SAS Data Sets
Creating a SAS Data Set from Raw Data
Data Values Separated by Delimiters