SAS for Data Analysis: Intermediate Statistical Methods (Statistics and Computing)

✍ Scribed by Mervyn G. Marasinghe, William J. Kennedy

• Publisher: Springer
• Year: 2008
• Tongue: English
• Leaves: 562
• Category: Library

✦ Synopsis

This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level. Generally, students enrolled in such courses are p- marily graduate majors or advanced undergraduate students from a variety of disciplines. These students typically have taken an introductory-level s- tistical methods course that requires the use a software system such as SAS for performing statistical analysis. Thus students are expected to have an - derstanding of basic concepts of statistical inference such as estimation and hypothesis testing. Understandably, adequate time is not available in a ?rst course in stat- tical methods to cover the use of a software system adequately in the amount of time available for instruction. The aim of this book is to teach how to use the SAS system for data analysis. The SAS language is introduced at a level of sophistication not found in most introductory SAS books. Important features such as SAS data step programming, pointers, and line-hold spe- ?ers are described in detail. The powerful graphics support available in SAS is emphasized throughout, and many worked SAS program examples contain graphic components.

✦ Table of Contents

Preface
Contents
Introduction to the SAS Language
Introduction
Basic Language: Rules and Syntax
Creating SAS Data Sets
The INPUT Statement
SAS Data Step Programming Statements and Their Uses
Data Step Processing
More on INPUT Statement
Use of pointer controls
The trailing @ line-hold specifier
The trailing @@ line-hold specifier
Use of RETAIN statement
The use of line pointer controls
Using SAS Procedures
Exercises
More on SAS Programming and Some Applications
More on the DATA and PROC Steps
Reading data from files
Combining SAS data sets
Saving and retrieving permanent SAS data sets
User-defined informats and formats
Creating SAS data sets in procedure steps
SAS Procedures for Computing Statistics
The UNIVARIATE procedure
The FREQ procedure
Some Useful Base SAS Procedures
The PLOT procedure
The CHART procedure
The TABULATE procedure
Exercises
Statistical Graphics Using SAS/GRAPH
Introduction
An Introduction to SAS/GRAPH
Useful SAS/GRAPH procedures
GPLOT procedure
GCHART procedure
Writing SAS/GRAPH programs
Quantile Plots
Empirical Quantile-Quantile Plots
Theoretical Quantile-Quantile Plots or Probability Plots
Profile Plots of Means or Interaction Plots
Two-Dimensional Scatter Plots and Scatter Plot Matrices
Two-Dimensional Scatter Plots
Scatter Plot Matrices
Histograms, Bar Charts, and Pie Charts
Other SAS Procedures for High-Resolution Graphics
Exercises
Statistical Analysis of Regression Models
An Introduction to Simple Linear Regression
Simple linear regression using PROC REG
Lack of fit test using PROC ANOVA
Diagnostic use of case statistics
Prediction of new y values using regression
An Introduction to Multiple Regression Analysis
Multiple regression analysis using PROC REG
Case statistics and residual analysis
Residual plots
Examining relationships among regression variables
Types of Sums of Squares Computed in PROC REG and PROC GLM
Model comparison technique and extra sum of squares
Types of sums of squares in SAS
Subset Selection Methods in Multiple Regression
Subset selection using PROC REG
Other options available in PROC REG for model selection
Inclusion of Squared Terms and Product Terms in Regression Models
Including interaction terms in the model
Comparing slopes of regression lines using interaction
Analysis of models with higher-order terms with PROC REG
Exercises
Analysis of Variance Models
Introduction
Treatment Structure
Experimental Designs
Linear Models
One-Way Classification
Using PROC ANOVA to analyze one-wayclassifications
Making preplanned (or a priori) comparisons using PROC GLM
Testing orthogonal polynomials using contrasts
One-Way Analysis of Covariance
Using PROC GLM to perform one-way covariance analysis
One-way covariance analysis: Testing for equal slopes
A Two-Way Factorial in a Completely Randomized Design
Analysis of a two-way factorial using PROC GLM
Residual analysis and transformations
Two-Way Factorial: Analysis of Interaction
Two-Way Factorial: Unequal Sample Sizes
Two-Way Classification: Randomized Complete Block Design
Using PROC GLM to analyze a RCBD
Using PROC GLM to test for nonadditivity
Exercises
Analysis of Variance: Random and Mixed Effects Models
Introduction
One-Way Random Effects Model
Using PROC GLM to analyze one-way random effects models
Using PROC MIXED to analyze one-way random effects models
Two-Way Crossed Random Effects Model
Using PROC GLM and PROC MIXED to analyze two-way crossed random effects models
Randomized complete block design: Blocking when treatment factors are random
Two-Way Nested Random Effects Model
Using PROC GLM to analyze two-way nested random effects models
Using PROC MIXED to analyze two-way nested random effects models
Two-Way Mixed Effects Model
Two-way mixed effects model: Randomized complete blocks design
Two-way mixed effects model: Crossed classification
Two-way mixed effects model: Nested classification
Models with Random and Nested Effects for More Complex Experiments
Models for nested factorials
Models for split-plot experiments
Analysis of split-plot experiments using PROC GLM
Analysis of split-plot experiments usingPROC MIXED
Exercises
APPENDICES
SAS/GRAPH
Introduction
SAS/GRAPH Statements
Goptions statement
SAS/GRAPH global statements
Printing and Exporting Graphics Output
Tables
References
Index