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目录：
Contents
1 A Brief Introduction to SAS
1.1 Introduction
1.2 The Microsoft Windows User Interface
1.2.1 The Editor Window
1.2.2 The Log and Output Windows
1.2.3 Other Menus
1.3 The SAS Language
1.3.1 All SAS Statements Must End with a Semicolon
1.3.2 Program Steps
1.3.3 Variable Names and Data Set Names
1.3.4 Variable Lists
1.4 The Data Step
1.4.1 Creating SAS Data Sets from Raw Data
1.4.2 The Data Statement
1.4.3 The Infile Statement
1.4.4 The Input Statement
1.4.5 Reading Data from an Existing SAS Data Set
1.4.6 Storing SAS Data Sets on Disk
1.5 Modifying SAS Data
1.5.1 Creating and Modifying Variables
1.5.2 Deleting Variables
1.5.3 Deleting Observations
1.5.4 Subsetting Data Sets
1.5.5 Concatenating and Merging Data Sets
1.5.6 Merging Data Sets: Adding Variables
1.5.7 The Operation of the Data Step
1.6 The
proc
Step
1.6.1 The
proc
Statement
1.6.2 The
var
Statement
©2002 CRC Press LLC
1.6.3 The
where
Statement
1.6.4 The
by
Statement
1.6.5 The
class
Statement
1.7 Global Statements
1.8 ODS: The Output Delivery System
1.9 SAS Graphics
1.9.1
Proc
gplot
1.9.2 Overlaid Graphs
1.9.3 Viewing and Printing Graphics
1.10 Some Tips for Preventing and Correcting Errors
2 Data Description and Simple Inference: Mortality and Water
Hardness in the U.K.
2.1 Description of Data
2.2 Methods of Analysis
2.3 Analysis Using SAS
Exercises
3 Simple Inference for Categorical Data: From Sandflies to
Organic Particulates in the Air
3.1 Description of Data
3.2 Methods of Analysis
3.3 Analysis Using SAS
3.3.1 Cross-Classifying Raw Data
3.3.2 Sandflies
3.3.3 Acacia Ants
3.3.4 Piston Rings
3.3.5 Oral Contraceptives
3.3.6 Oral Cancers
3.3.7 Particulates and Bronchitis
Exercises
4 Multiple Regression: Determinants of Crime Rate in the
United States
4.1 Description of Data
4.2 The Multiple Regression Model
4.3 Analysis Using SAS
Exercises
5 Analysis of Variance I: Treating Hypertension
5.1 Description of Data
5.2 Analysis of Variance Model
5.3 Analysis Using SAS
©2002 CRC Press LLC
Exercises
6 Analysis of Variance II: School Attendance Amongst
Australian Children
6.1 Description of Data
6.2 Analysis of Variance Model
6.2.1 Type I Sums of Squares
6.2.2 Type III Sums of Squares
6.3 Analysis Using SAS
Exercises
7 Analysis of Variance of Repeated Measures: Visual Acuity
7.1 Description of Data
7.2 Repeated Measures Data
7.3 Analysis of Variance for Repeated Measures Designs
7.4 Analysis Using SAS
Exercises
8 Logistic Regression: Psychiatric Screening, Plasma Proteins, and
Danish Do-It-Yourself
8.1 Description of Data
8.2 The Logistic Regression Model
8.3 Analysis Using SAS
8.3.1 GHQ Data
8.3.2 ESR and Plasma Levels
8.3.3 Danish Do-It-Yourself
Exercises
9 Generalised Linear Models: School Attendance Amongst
Australian School Children
9.1 Description of Data
9.2 Generalised Linear Models
9.2.1 Model Selection and Measure of Fit
9.3 Analysis Using SAS
Exercises
10 Longitudinal Data I: The Treatment of Postnatal Depression
10.1 Description of Data
10.2 The Analyses of Longitudinal Data
10.3 Analysis Using SAS
10.3.1 Graphical Displays
10.3.2 Response Feature Analysis
Exercises
©2002 CRC Press LLC
11 Longitudinal Data II: The Treatment of Alzheimer’s Disease
11.1 Description of Data
11.2 Random Effects Models
11.3 Analysis Using SAS
Exercises
12 Survival Analysis: Gastric Cancer and Methadone Treatment
of Heroin Addicts
12.1 Description of Data
12.2 Describing Survival and Cox’s Regression Model
12.2.1 Survival Function
12.2.2 Hazard Function
12.2.3 Cox’s Regression
12.3 Analysis Using SAS
12.3.1 Gastric Cancer
12.3.2 Methadone Treatment of Heroin Addicts
Exercises
13 Principal Components Analysis and Factor Analysis:
The Olympic Decathlon and Statements about Pain
13.1 Description of Data
13.2 Principal Components and Factor Analyses
13.2.1 Principal Components Analysis
13.2.2 Factor Analysis
13.2.3 Factor Analysis and Principal Components
Compared
13.3 Analysis Using SAS
13.3.1 Olympic Decathlon
13.3.2 Statements about Pain
Exercises
14 Cluster Analysis: Air Pollution in the U.S.A.
14.1 Description of Data
14.2 Cluster Analysis
14.3 Analysis Using SAS
Exercises
15 Discriminant Function Analysis: Classifying Tibetan Skulls
15.1 Description of Data
15.2 Discriminant Function Analysis
15.3 Analysis Using SAS
Exercises
©2002 CRC Press LLC
16 Correspondence Analysis: Smoking and Motherhood,
Sex and the Single Girl, and European Stereotypes
16.1 Description of Data
16.2 Displaying Contingency Table Data Graphically Using
Correspondence Analysis
16.3 Analysis Using SAS
16.3.1 Boyfriends
16.3.2 Smoking and Motherhood
16.3.3 Are the Germans Really Arrogant?
Exercises
Appendix A: SAS Macro to Produce Scatterplot Matrices
Appendix B: Answers to Selected Chapter Exercises
References