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Analysis of Messy Data

by: Milliken, George A.; Johnson, Dallas E.

Analysis of Messy Data

by: Milliken, George A.; Johnson, Dallas E.

ISBN 13:
9780412990816
ISBN 10:
0412990814
Edition: Reprint
Format: Hardcover
Copyright: 07/01/1992
Publisher: Chapman & Hall
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Summary

This classic reference details methods for effectively analyzing non-standard or messy data sets. The authors introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. They emphasize the distinction between design structure and the structure of treatments and focus on using the techniques with several statistical packages, including SAS, BMDP, and SPSS.

Preface

The Simplest Case --- One-Way Treatment Structure in a Completely Randomized Design Structure with Homogeneous Errors

(15)

Model Definitions and Assumptions

(1)

Parameter Estimation

(1)

Inferences on Linear Combinations --- Tests and Confidence Intervals

(4)

Simultaneous Tests on Several Linear Combinations

(3)

Comparing All Means

(2)

General Method for Comparing Two Models---Principle of Conditional Error

(2)

Computer Analyses

(1)

One-Way Treatment Structure in a Completely Randomized Design Structure with Heterogeneous Errors

(13)

Model Definition and Assumptions

(1)

Parameter Estimation

(1)

Tests for Homogeneity of Variances

(5)

Inferences on Linear Combinations---Tests and Confidence Intervals

(3)

Comparing All Means

(3)

Simultaneous Inference Procedures and Multiple Comparisons

(17)

Error Rates

(1)

Recommendations

(1)

Least Significant Differences

(1)

Fisher's LSD Procedure

(1)

Bonferroni's Method

(1)

Multivariate t-Method

(1)

Scheffe's Procedure

(1)

Tukey's Honest Significant Difference

(1)

Examples

(3)

Newman - Keuls Method

(2)

Duncan's New Multiple Range Method

(2)

Waller-Duncan Procedure

(1)

A Caution

(2)

Basics of Experimental Design

(21)

Introduction---Basic Ideas

(3)

Structures of an Experimental Design

(4)

Examples of Different Experimental Designs

(13)

Experimental Designs Involving Several Sizes of Experimental Units

(25)

Identifying the Sizes of Experimental Units

(1)

Split-Plot Type Designs

(11)

Repeated Measures Designs

(7)

Designs Involving Nested Factors

(5)

Matrix Form of the Model

(18)

Basic Notation

(4)

Least Squares Estimation

(7)

Estimability and Connected Designs

104

(2)

Testing Hypotheses about Linear Model Parameters

106

(1)

Population Marginal Means

107

(3)

Balanced Two-Way Treatment Structures

110

(5)

Model Definition and Assumptions

111

(1)

Parameter Estimation

112

(1)

Interactions and Their Importance

113

(1)

Main Effects

113

(1)

Computer Analyses

113

(2)

Case Study: Complete Analyses of Balanced Two-Way Experiments

115

(11)

Contrasts of Main Effects

116

(1)

Contrasts of Interaction Effects

117

(1)

Paint-Paving Example

118

(4)

Analyzing Quantitative Treatment Factors

122

(2)

Multiple Comparisons

124

(2)

Using the Means Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers

126

(11)

Model Definitions and Assumptions

127

(1)

Parameter Estimation

127

(1)

Testing Whether All Means Are Equal

128

(2)

Interaction and Main-Effect Hypotheses

130

(2)

Population Marginal Means

132

(2)

Simultaneous Inferences and Multiple Comparisons

134

(1)

Computer Analyses

135

(2)

Using the Effects Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers

137

(22)

Model Definition

138

(1)

Parameter Estimates and Type I Analysis

138

(4)

Using Estimable Functions in SAS®

142

(4)

Type I through Type IV Hypotheses

146

(5)

Using Type I through Type IV Estimable Functions in SAS®

151

(5)

Population Marginal Means and Least Squares Means

156

(1)

Computer Analyses

156

(3)

Analyzing Large Balanced Two-Way Experiments with Unequal Subclass Numbers

159

(6)

Feasibility Problems

160

(1)

Method of Unweighted Means

160

(2)

Simultaneous Inference and Multiple Comparisons

162

(1)

Example of Method of Unweighted Means

162

(1)

Computer Analyses

163

(2)

Case Study: Balanced Two-Way Treatment Structure with Unequal Subclass Numbers

165

(7)

Fat-Surfactant Example

166

(6)

Using the Means Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations

172

(6)

Parameter Estimation

173

(1)

Hypothesis Testing and Confidence Intervals

174

(3)

Computer Analyses

177

(1)

Using the Effects Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations

178

(13)

Type I and Type II Hypotheses

179

(5)

Type III Hypotheses

184

(2)

Type IV Hypotheses

186

(2)

Population Marginal Means and Least Squares Means

188

(1)

Computer Analyses

189

(2)

Case Study: Two-Way Treatment Structure with Missing Treatment Combinations

191

(5)

Analyzing Three-Way and Higher-Order Treatment Structures

196

(5)

General Strategy

197

(2)

Balanced and Unbalanced Experiments

199

(2)

Case Study: Three-Way Treatment Structure with Many Missing Treatment Combinations

201

(10)

Nutrition Scores Example

202

(1)

An SAS® GLM Analysis

203

(2)

A Complete Analysis

205

(6)

Random Models and Variance Components

211

(21)

Introduction

212

(2)

General Random Model in Matrix Form

214

(2)

Computing Expected Mean Squares

216

(16)

Methods for Estimating Variance Components

232

(14)

Method of Momentss

233

(6)

Maximum Likelihood Estimators

239

(2)

MINQUE Method

241

(5)

Methods for Making Inference about Variance Components

246

(17)

Testing Hypotheses

247

(8)

Constructing Confidence Intervals

255

(7)

Modified Method-of-Moments Estimation Procedure

262

(1)

Case Study: Analysis of a Random Model

263

(11)

Data

264

(1)

Estimation

264

(1)

Model Building

264

(6)

Confidence Intervals

270

(4)

Analysis of Mixed Models

274

(10)

Introduction to Mixed Models

275

(1)

Analysis of Random Part of Mixed Model

276

(3)

Analysis of Fixed Part of Mixed Model

279

(5)

Two Case Studies of a Mixed Model

284

(12)

Balanced Two-Way Model

285

(4)

Unbalanced Two-Way-Model

289

(7)

Methods for Analyzing Balanced Split-Plot Designs

296

(19)

Model Definition and Parameter Estimation

297

(3)

Standard Errors for Multiple Comparisons

300

(4)

General Method for Computing Standard Errors for Multiple Comparisons

304

(2)

Comparisons via Contrasts

306

(9)

Strip-Plot Experimental Designs

315

(7)

Description of a Strip-Plot Design and Model

316

(2)

Techniques for Making Inferences

318

(3)

Example of a Strip Plot

321

(1)

Analysis of Repeated Measures Designs for Which the Usual Assumptions Hold

322

(29)

Model Specifications and Assumptions

323

(3)

The Usual Analysis of Variance

326

(25)

Analysis of Repeated Measures Designs for Which the Usual Assumptions Do Not Hold

351

(26)

When the Assumptions Do Not Hold

352

(11)

A Two-Factor Model with Autocorrelated Errors

363

(14)

Analyzing Split-Plot and Certain Repeated Measures Experiments with Unbalanced and Missing Data

377

(31)

Unequal Numbers of Whole Plot Experimental Units

378

(6)

Unequal Numbers of Subplot Experimental Units

384

(24)

Computing the Variances of Contrasts for Repeated Measures and Split-Plot Designs by Using Hartley's Method of Synthesis

408

(5)

Using Hartley's Method of Synthesis

409

(1)

A Simple Example

410

(3)

Analysis of Nested Designs

413

(11)

Definitions, Assumptions, and Models

414

(2)

Parameter Estimation

416

(5)

Testing Hypotheses and Confidence Intervals

421

(3)

Analysis of Repeated Measures Experiments by Using Multivariate Methods

424

(9)

General Multivariate Model

425

(1)

Hypothesis Testing

426

(1)

A Repeated Measures Example

427

(4)

Marginal Means and Special Linear Combinations

431

(2)

Analysis of Crossover Designs

433

(19)

Definitions, Assumptions, and Models

434

(1)

Two-Period Crossover Designs

435

(5)

Crossover Designs with More than Two Periods

440

(12)

Appendices

452

(13)

References

465

(4)

Index

469

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Analysis of Messy Data

Analysis of Messy Data

9780412990816

0412990814

Summary

Table of Contents

Supplemental Materials

Write a Review

Amazon no longer offers textbook rentals. We do!

Analysis of Messy Data

Analysis of Messy Data

9780412990816

0412990814

Summary

Table of Contents

Supplemental Materials

Write a Review

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