SAS for Linear Models, Fourth Edition

ISBN-10: 1590470230

ISBN-13: 9781590470237

Edition: 4th 2002

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Book details

List price: $66.95
Edition: 4th
Copyright year: 2002
Publisher: SAS Institute
Binding: Hardcover
Pages: 492
Size: 8.75" wide x 11.00" long x 1.00" tall
Weight: 3.058
Language: English

Acknowledgments
Introduction
About This Book
Statistical Topics and SAS Procedures
Regression
Introduction
The REG Procedure
Using the REG Procedure to Fit a Model with One Independent Variable
The P, CLM, and CLI Options: Predicted Values and Confidence Limits
A Model with Several Independent Variables
The SS1 and SS2 Options: Two Types of Sums of Squares
Tests of Subsets and Linear Combinations of Coefficients
Fitting Restricted Models: The RESTRICT Statement and NOINT Option
Exact Linear Dependency
The GLM Procedure
Using the GLM Procedure to Fit a Linear Regression Model
Using the CONTRAST Statement to Test Hypotheses about Regression Parameters
Using the ESTIMATE Statement to Estimate Linear Combinations of Parameters
Statistical Background
Terminology and Notation
Partitioning the Sums of Squares
Hypothesis Tests and Confidence Intervals
Using the Generalized Inverse
Analysis of Variance for Balanced Data
Introduction
One- and Two-Sample Tests and Statistics
One-Sample Statistics
Two Related Samples
Two Independent Samples
The Comparison of Several Means: Analysis of Variance
Terminology and Notation
Crossed Classification and Interaction Sum of Squares
Nested Effects and Nested Sum of Squares
Using the ANOVA and GLM Procedures
Multiple Comparisons and Preplanned Comparisons
The Analysis of One-Way Classification of Data
Computing the ANOVA Table
Computing Means, Multiple Comparisons of Means, and Confidence Intervals
Planned Comparisons for One-Way Classification: The CONTRAST Statement
Linear Combinations of Model Parameters
Testing Several Contrasts Simultaneously
Orthogonal Contrasts
Estimating Linear Combinations of Parameters: The Estimate Statement
Randomized-Blocks Designs
Analysis of Variance for Randomized-Blocks Design
Additional Multiple Comparison Methods
Dunnett's Test to Compare Each Treatment to a Control
A Latin Square Design with Two Response Variables
A Two-Way Factorial Experiment
ANOVA for a Two-Way Factorial Experiment
Multiple Comparisons for a Factorial Experiment
Multiple Comparisons of METHOD Means by VARIETY
Planned Comparisons in a Two-Way Factorial Experiment
Simple Effect Comparisons
Main Effect Comparisons
Simultaneous Contrasts in Two-Way Classifications
Comparing Levels of One Factor within Subgroups of Levels of Another Factor
An Easier Way to Set Up CONTRAST and ESTIMATE Statements
Analyzing Data with Random Effects
Introduction
Nested Classifications
Analysis of Variance for Nested Classifications
Computing Variances of Means from Nested Classifications and Deriving Optimum Sampling Plans
Analysis of Variance for Nested Classifications: Using Expected Mean Squares to Obtain Valid Tests of Hypotheses
Variance Component Estimation for Nested Classifications: Analysis Using PROC MIXED
Additional Analysis of Nested Classifications Using PROC MIXED: Overall Mean and Best Linear Unbiased Prediction
Blocked Designs with Random Blocks
Random-Blocks Analysis Using PROC MIXED
Differences between GLM and MIXED Randomized-Complete-Blocks Analysis: Fixed versus Random Blocks
Treatment Means
Treatment Differences
The Two-Way Mixed Model
Analysis of Variance for the Two-Way Mixed Model: Working with Expected Mean Squares to Obtain Valid Tests
Standard Errors for the Two-Way Mixed Model: GLM versus MIXED
More on Expected Mean Squares: Determining Quadratic Forms and Null Hypotheses for Fixed Effects
A Classification with Both Crossed and Nested Effects
Analysis of Variance for Crossed-Nested Classification
Using Expected Mean Squares to Set Up Several Tests of Hypotheses for Crossed-Nested Classification
Satterthwaite's Formula for Approximate Degrees of Freedom
PROC MIXED Analysis of Crossed-Nested Classification
Split-Plot Experiments
A Standard Split-Plot Experiment
Analysis of Variance Using PROC GLM
Analysis with PROC MIXED
Unbalanced Data Analysis: Basic Methods
Introduction
Applied Concepts of Analyzing Unbalanced Data
ANOVA for Unbalanced Data
Using the CONTRAST and Estimate Statements with Unbalanced Data
The LSMEANS Statement
More on Comparing Means: Other Hypotheses and Types of Sums of Squares
Issues Associated with Empty Cells
The Effect of Empty Cells on Types of Sums of Squares
The Effect of Empty Cells on CONTRAST, ESTIMATE, and LSMEANS Results
Some Problems with Unbalanced Mixed-Model Data
Using the GLM Procedure to Analyze Unbalanced Mixed-Model Data
Approximate F-Statistics from ANOVA Mean Squares with Unbalanced Mixed-Model Data
Using the CONTRAST, ESTIMATE, and LSMEANS Statements in GLM with Unbalanced Mixed-Model Data
Using the MIXED Procedure to Analyze Unbalanced Mixed-Model Data
Using the GLM and MIXED Procedures to Analyze Mixed-Model Data with Empty Cells
Summary and Conclusions about Using the GLM and MIXED Procedures to Analyze Unbalanced Mixed-Model Data
Understanding Linear Models Concepts
Introduction
The Dummy-Variable Model
The Simplest Case: A One-Way Classification
Parameter Estimates for a One-Way Classification
Using PROC GLM for Analysis of Variance
Estimable Functions in a One-Way Classification
Two-Way Classification: Unbalanced Data
General Considerations
Sums of Squares Computed by PROC GLM
Interpreting Sums of Squares in Reduction Notation
Interpreting Sums of Squares in [mu]-Model Notation
An Example of Unbalanced Two-Way Classification
The MEANS, LSMEANS, CONTRAST, and ESTIMATE Statements in a Two-Way Layout
Estimable Functions for a Two-Way Classification
The General Form of Estimable Functions
Interpreting Sums of Squares Using Estimable Functions
Estimating Estimable Functions
Interpreting LSMEANS, CONTRAST, and ESTIMATE Results Using Estimable Functions
Empty Cells
Mixed-Model Issues
Proper Error Terms
More on Expected Mean Squares
An Issue of Model Formulation Related to Expected Mean Squares
ANOVA Issues for Unbalanced Mixed Models
Using Expected Mean Squares to Construct Approximate F-Tests for Fixed Effects
GLS and Likelihood Methodology Mixed Model
An Overview of Generalized Least Squares Methodology
Some Practical Issues about Generalized Least Squares Methodology
Analysis of Covariance
Introduction
A One-Way Structure
Covariance Model
Means and Least-Squares Means
Contrasts
Multiple Covariates
Unequal Slopes
Testing the Heterogeneity of Slopes
Estimating Different Slopes
Testing Treatment Differences with Unequal Slopes
A Two-Way Structure without Interaction
A Two-Way Structure with Interaction
Orthogonal Polynomials and Covariance Methods
A 2 x 3 Example
Use of the IML ORPOL Function to Obtain Orthogonal Polynomial Contrast Coefficients
Use of Analysis of Covariance to Compute ANOVA and Fit Regression
Repeated-Measures Analysis
Introduction
The Univariate ANOVA Method for Analyzing Repeated Measures
Using GLM to Perform Univariate ANOVA of Repeated-Measures Data
The CONTRAST, ESTIMATE, and LSMEANS Statements in Univariate ANOVA of Repeated-Measures Data
Multivariate and Univariate Methods Based on Contrasts of the Repeated Measures
Univariate ANOVA of Repeated Measures at Each Time
Using the REPEATED Statement in PROC GLM to Perform Multivariate Analysis of Repeated-Measures Data
Univariate ANOVA of Contrasts of Repeated Measures
Mixed-Model Analysis of Repeated Measures
The Fixed-Effects Model and Related Considerations
Selecting an Appropriate Covariance Model
Reassessing the Covariance Structure with a Means Model Accounting for Baseline Measurement
Information Criteria to Compare Covariance Models
PROC MIXED Analysis of FEV1 Data
Inference on the Treatment and Time Effects of FEV1 Data Using PROC MIXED
Comparisons of DRUG*HOUR Means
Comparisons Using Regression
Multivariate Linear Models
Introduction
A One-Way Multivariate Analysis of Variance
Hotelling's T[superscript 2] Test
A Two-Factor Factorial
Multivariate Analysis of Covariance
Contrasts in Multivariate Analyses
Statistical Background
Generalized Linear Models
Introduction
The Logistic and Probit Regression Models
Logistic Regression: The Challenger Shuttle O-Ring Data Example
Using the Inverse Link to Get the Predicted Probability
Alternative Logistic Regression Analysis Using 0-1 Data
An Alternative Link: Probit Regression
Binomial Models for Analysis of Variance and Analysis of Covariance
Logistic ANOVA
The Analysis-of-Variance Model with a Probit Link
Logistic Analysis of Covariance
Count Data and Overdispersion
An Insect Count Example
Model Checking
Correction for Overdispersion
Fitting a Negative Binomial Model
Using PROC GENMOD to Fit the Negative Binomial with a Log Link
Fitting the Negative Binomial with a Canonical Link
Advanced Application: A User-Supplied Program to Fit the Negative Binomial with a Canonical Link
Generalized Linear Models with Repeated Measures--Generalized Estimating Equations
A Poisson Repeated-Measures Example
Using PROC GENMOD to Compute a GEE Analysis of Repeated Measures
Background Theory
The Generalized Linear Model Defined
How the GzLM's Parameters Are Estimated
Standard Errors and Test Statistics
Quasi-Likelihood
Repeated Measures and Generalized Estimating Equations
Examples of Special Applications
Introduction
Confounding in a Factorial Experiment
Confounding with Blocks
A Fractional Factorial Example
A Balanced Incomplete-Blocks Design
A Crossover Design with Residual Effects
Models for Experiments with Qualitative and Quantitative Variables
A Lack-of-Fit Analysis
An Unbalanced Nested Structure
An Analysis of Multi-Location Data
An Analysis Assuming No LocationxTreatment Interaction
A Fixed-Location Analysis with an Interaction
A Random-Location Analysis
Further Analysis of a LocationxTreatment Interaction Using a Location Index
Absorbing Nesting Effects
References
Index
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