SAS Statistics by Example

Ron Cody Edd

Description

In SAS Statistics by Example, Ron Cody offers up a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--this book provides an easy-to-follow, how-to approach to statistical analysis not found in other books.
For each statistical task, Cody includes heavily annotated examples using ODS Statistical Graphics procedures such as SGPLOT, SGSCATTER, and SGPANEL that show how SAS can produce the required statistics. Also, you will learn how to test the assumptions for all relevant statistical tests. Major topics featured include descriptive statistics, one- and two-sample tests, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and sample size.
This is not a book that teaches statistics. Rather, SAS Statistics by Example is perfect for intermediate to advanced statistical programmers who know their statistics and want to use SAS to do their analyses.
This book is part of the SAS Press program.

Sujets

• Getting Started
• Statistical model
• Statistics

Informations

• Publié par: SAS Institute
• Date de parution: 22 août 2011
• Nombre de lectures: 1
• EAN13: 9781612900124
• Langue: English
• Poids de l'ouvrage: 15 Mo

Extrait

Copyright
The correct bibliographic citation for this manual is as follows: Cody, Ron. 2011. SAS ® Statistics by Example. Cary, NC: SAS Institute Inc.
SAS ® Statistics by Example
Copyright © 2011, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-61290-012-4 (electronic book) ISBN 978-1-60764-800-0
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1st printing, August 2011
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Contents
List of Programs
Acknowledgments
Chapter 1 An Introduction to SAS
Introduction
What is SAS
Statistical Tasks Performed by SAS
The Structure of SAS Programs
SAS Data Sets
SAS Display Manager
Excel Workbooks
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
Reading CSV Files
Data Values in Fixed Columns
Excel Files with Invalid SAS Variable Names
Other Sources of Data
Conclusions
Chapter 2 Descriptive Statistics – Continuous Variables
Introduction
Computing Descriptive Statistics Using PROC MEANS
Descriptive Statistics Broken Down by a Classification Variable
Computing a 95% Confidence Interval and the Standard Error
Producing Descriptive Statistics, Histograms, and Probability Plots
Changing the Midpoint Values on the Histogram
Generating a Variety of Graphical Displays of Your Data
Displaying Multiple Box Plots for Each Value of a Categorical Variable
Conclusions
Chapter 3 Descriptive Statistics – Categorical Variables
Introduction
Computing Frequency Counts and Percentages
Computing Frequencies on a Continuous Variable
Using Formats to Group Observations
Histograms and Bar Charts
Creating a Bar Chart Using PROC SGPLOT
Using ODS to Send Output to Alternate Destinations
Creating a Cross-Tabulation Table
Changing the Order of Values in a Frequency Table
Conclusions
Chapter 4 Descriptive Statistics – Bivariate Associations
Introduction
Producing a Simple Scatter Plot Using PROG GPLOT
Producing a Scatter Plot Using PROC SGPLOT
Creating Multiple Scatter Plots on a Single Page Using PROC SGSCATTER
Conclusions
Chapter 5 Inferential Statistics – One-Sample Tests
Introduction
Conducting a One-Sample t -test Using PROC TTEST
Running PROC TTEST with ODS Graphics Turned On
Conducting a One-Sample t -test Using PROC UNIVARIATE
Testing Whether a Distribution is Normally Distributed
Tests for Other Distributions
Conclusions
Chapter 6 Inferential Statistics – Two-Sample Tests
Introduction
Conducting a Two-Sample t -test
Testing the Assumptions for a t -test
Customizing the Output from ODS Statistical Graphics
Conducting a Paired t -test
Assumption Violations
Conclusions
Chapter 7 Inferential Statistics – Comparing More than Two Means
Introduction
A Simple One-way Design
Conducting Multiple Comparison Tests
Using ODS Graphics to Produce a Diffogram
Two-way Factorial Designs
Analyzing Factorial Models with Significant Interactions
Analyzing a Randomized Block Design
Conclusions
Chapter 8 Correlation and Regression
Introduction
Producing Pearson Correlations
Generating a Correlation Matrix
Creating HTML Output with Data Tips
Generating Spearman Nonparametric Correlations
Running a Simple Linear Regression Model
Using ODS Statistical Graphics to Investigate Influential Observations
Using the Regression Equation to Do Prediction
A More Efficient Way to Compute Predicted Values
Conclusions
Chapter 9 Multiple Regression
Introduction
Fitting Multiple Regression Models
Running All Possible Regressions with n Variables
Producing Separate Plots Instead of a Panel
Choosing the Best Model (C p and Hocking’s Criteria)
Forward, Backward, and Stepwise Selection Methods
Forcing Selected Variables into a Model
Creating Dummy (Design) Variables for Regression
Detecting Collinearity
Influential Observations in Multiple Regression Models
Conclusions
Chapter 10 Categorical Data
Introduction
Comparing Proportions
Rearranging Rows and Columns in a Table
Tables with Expected Values Less Than 5 (Fisher’s Exact Test)
Computing Chi-Square from Frequency Data
Using a Chi-Square Macro
A Short-Cut Method for Requesting Multiple Tables
Computing Coefficient Kappa—A Test of Agreement
Computing Tests for Trends
Computing Chi-Square for One-Way Tables
Conclusions
Chapter 11 Binary Logistic Regression
Introduction
Running a Logistic Regression Model with One Categorical Predictor Variable
Running a Logistic Regression Model with One Continuous Predictor Variable
Using a Format to Create a Categorical Variable from a Continuous Variable
Using a Combination of Categorical and Continuous Variables in a Logistic Regression Model
Running a Logistic Regression with Interactions
Conclusions
Chapter 12 Nonparametric Tests
Introduction
Performing a Wilcoxon Rank-Sum Test
Performing a Wilcoxon Signed-Rank Test (for Paired Data)
Performing a Kruskal-Wallis One-Way ANOVA
Comparing Spread: The Ansari-Bradley Test
Converting Data Values into Ranks
Using PROC RANK to Group Your Data Values
Conclusions
Chapter 13 Power and Sample Size
Introduction
Computing the Sample Size for an Unpaired t -Test
Computing the Power of an Unpaired t -Test
Computing Sample Size for an ANOVA Design
Computing Sample Sizes (or Power) for a Difference in Two Proportions
Using the SAS Power and Sample Size Interactive Application
Conclusions
Chapter 14 Selecting Random Samples
Introduction
Taking a Simple Random Sample
Taking a Random Sample with Replacement
Creating Replicate Samples using PROC SURVEYSELECT
Conclusions
References
Index




List of Programs
Chapter 1
Program 1.1: Using PROC PRINT to List the Observations in a SAS Data Set
Program 1.2: Using PROC CONTENTS to Display the Data Descriptor Portion of a SAS Data Set
Program 1.3: Reading Data from a Text File That Uses Spaces as Delimiters
Program 1.4: Using PROC PRINT to List the Observations in Data Set Sample2
Program 1.5: Reading a CSV File
Chapter 2
Program 2.1: Generating Descriptive Statistics with PROC MEANS
Program 2.2: Statistics Broken Down by a Classification Variable
Program 2.3: Demonstrating the PRINTALLTYPES Option with PROC MEANS
Program 2.4: Computing a 95% Confidence Interval
Program 2.5: Producing Histograms and Probability Plots Using PROC UNIVARIATE
Program 2.6: Using PROC SGPLOT to Produce a Histogram
Program 2.7: Using SGPLOT to Produce a Horizontal Box Plot
Program 2.8: Displaying Outliers in a Box Plot
Program 2.9: Labeling Outliers on a Box Plot
Program 2.10: Displaying Multiple Box Plots for Each Value of a Categorical Variable
Chapter 3
Program 3.1: Computing Frequencies and Percentages Using PROC FREQ
Program 3.2: Demonstrating the NOCUM Tables Option
Program 3.3: Demonstrating the Effect of the MISSING Option with PROC FREQ
Program 3.4: Computing Frequencies on a Continuous Variable
Program 3.5: Writing a Format for Gender, SBP, and DBP
Program 3.6: Generating a Bar Chart Using PROC GCHART
Program 3.7: Generating a Bar Chart Using PROC SGPLOT
Program