Design and Analysis of Experiments with SAS / Edition 1

Design and Analysis of Experiments with SAS / Edition 1

by John Lawson
     
 

ISBN-10: 1420060600

ISBN-13: 9781420060607

Pub. Date: 04/22/2010

Publisher: Taylor & Francis

A culmination of the author’s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows

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Overview

A culmination of the author’s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.

Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples of experiments and exercises that enable students to perform their own experiments. Harnessing the capabilities of SAS 9.2, it includes examples of SAS data step programming and IML, along with procedures from SAS Stat, SAS QC, and SAS OR. The text also shows how to display experimental results graphically using SAS ODS graphics. The author emphasizes how the sample size, the assignment of experimental units to combinations of treatment factor levels (error control), and the selection of treatment factor combinations (treatment design) affect the resulting variance and bias of estimates as well as the validity of conclusions.

This textbook covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design choice, the practical aspects of creating a design and performing experiments, and the interpretation of the results of computer data analysis. SAS code and ancillaries are available at http://lawson.mooo.com

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Product Details

ISBN-13:
9781420060607
Publisher:
Taylor & Francis
Publication date:
04/22/2010
Series:
Chapman & Hall/CRC Texts in Statistical Science Series, #85
Edition description:
New Edition
Pages:
596
Product dimensions:
6.40(w) x 9.30(h) x 1.40(d)

Related Subjects

Table of Contents

Preface

1 Introduction 1

1.1 Statistics and Data Collection 1

1.2 Beginnings of Statistically Planned Experiments 2

1.3 Definitions and Preliminaries 2

1.4 Purposes of Experimental Design 5

1.5 Types of Experimental Designs 6

1.6 Planning Experiments 7

1.7 Performing the Experiments 9

1.8 Use of SAS Software 11

1.9 Review of Important Concepts 12

1.10 Exercises 14

2 Completely Randomized Designs with One Factor 15

2.1 Introduction 15

2.2 Replication and Randomization 15

2.3 A Historical Example 18

2.4 Linear Model for CRD 19

2.5 Verifying Assumptions of the Linear Model 27

2.6 Analysis Strategies When Assumptions Are Violated 30

2.7 Determining the Number of Replicates 37

2.8 Comparison of Treatments after the F-Zest 41

2.9 Review of Important Concepts 48

2.10 Exercises 50

3 Factorial Designs 53

3.1 Introduction 53

3.2 Classical One at a Time versus Factorial Plans 53

3.3 Interpreting Interactions 55

3.4 Creating a Two-Factor Factorial Plan in SAS 58

3.5 Analysis of a Two-Factor Factorial in SAS 60

3.6 Factorial Designs with Multiple Factors - CRFD 80

3.7 Two-Level Factorials 86

3.8 Verifying Assumptions of the Model 102

3.9 Review of Important Concepts 106

3.10 Exercises 108

3.11 Appendix-SAS Macro for Turkey's Single df Test 112

4 Randomised Block Designs 115

4.1 Introduction 115

4.2 Creating an RCB in SAS 116

4.3 Model for RCB 119

4.4 An Example of an RCB 121

4.5 Determining the Number of Blocks 124

4.6 Factorial Designs in Blocks 125

4.7 Generalized Complete Block Design 128

4.8 Two Block Factors LSD 131

4.9 Review of Important Concepts 138

4.10 Exercises 140

4.11 Appendix-Data from Golf Experiment 145

5 Designs to Study Variances 147

5.1 Introduction 147

5.2 Random Factors and Random Sampling Experiments 148

5.3 One-Factor Sampling Designs 150

5.4 Estimating Variance Components 151

5.5 Two-Factor Sampling Designs 161

5.6 Nested Sampling Experiments (NSE) 170

5.7 Staggered Nested Designs 173

5.8 Designs with Fixed and Random Factors 179

5.9 Graphical Methods to Check Model Assumptions 186

5.10 Review of Important Concepts 194

5.11 Exercises 196

5.12 Appendix 198

6 Fractional Factorial Designs 199

6.1 Introduction 199

6.2 Half-Fractions of 2k Designs 200

6.3 Quarter and Higher Fractions of 2k Designs 209

6.4 Criteria for Choosing Generators for 2k-P Designs 211

6.5 Augmenting Fractional Factorials 222

6.6 Plackett-Burman (PB) Screening Designs 232

6.7 Mixed Level Factorials and Orthogonal Arrays (OA) 238

6.8 Review of Important Concepts 246

6.9 Exercises 248

7 Incomplete and Confounded Block Designs 255

7.1 Introduction 255

7.2 Balanced Incomplete Block (BIB) Designs 256

7.3 Analysis of Incomplete Block Designs 259

7.4 PBIB-BTIB Designs 261

7.5 Youden Square Designs (YSD) 265

7.6 Confounded 2k and 2k-p Designs 266

7.7 Confounding 3 Level and p Level Factorial Designs 280

7.8 Blocking Mixed-Level Factorials and OAs 283

7.9 Partially Confounded Blocked Factorial (PCBF) 290

7.10 Review of Important Concepts 295

7.11 Exercises 298

8 Split-Plot Designs 301

8.1 Introduction 301

8.2 Split-Plot Experiments with CRD in Whole Plots CRSP 302

8.3 RCB in Whole Plots RBSP 309

8.4 Analysis Unreplicated 2k Split-Plot Designs 318

8.5 2k-P Fractional Factorials in Split Plots (FFSP) 324

8.6 Sample Size and Power Issues for Split-Plot Designs 338

8.7 Review of Important Concepts 339

8.8 Exercises 341

9 Crossover and Repeated Measures Designs 347

9.1 Introduction 347

9.2 Crossover Designs (COD) 347

9.3 Simple AB, BA Crossover Designs for Two Treatments 348

9.4 Crossover Designs for Multiple Treatments 358

9.5 Repeated Measures Designs 364

9.6 Univariate Analysis of Repeated Measures Design 365

9.7 Review of Important Concepts 374

9.8 Exercises 376

10 Response Surface Designs 381

10.1 Introduction 381

10.2 Fundamentals of Response Surface Methodology 381

10.3 Standard Designs for Second Order Models 385

10.4 Creating Standard Designs in SAS 392

10.5 Non-Standard Response Surface Designs 395

10.6 Fitting the Response Surface Model with SAS 403

10.7 Determining Optimum Operating Conditions 410

10.8 Blocked Response Surface (BRS) Designs 421

10.9 Response Surface Split-Plot (RSSP) Designs 424

10.10 Review of Important Concepts 435

10.11 Exercises 437

11 Mixture Experiments 443

11.1 Introduction 443

11.2 Models and Designs for Mixture Experiments 445

11.3 Creating Mixture Designs in SAS 452

11.4 Analysis of Mixture Experiment 454

11.5 Constrained Mixture Experiments 461

11.6 Blocking Mixture Experiments 470

11.7 Mixture Experiments with Process Variables 475

11.8 Mixture Experiments in Split-Plot Arrangements 484

11.9 Review of Important Concepts 487

11.10 Exercises 489

11.11 Appendix Example of Fitting Independent Factors 498

12 Robust Parameter Design Experiments 501

12.1 Introduction 501

12.2 Noise-Sources of Functional Variation 502

12.3 Product Array Parameter Design Experiments 504

12.4 Analysis of Product Array Experiments 512

12.5 Single Array Parameter Design Experiments 529

12.6 Joint Modeling of Mean and Dispersion Effects 538

12.7 Review of Important Concepts 545

12.8 Exercises 547

13 Experimental Strategies for Increasing Knowledge 555

13.1 Introduction 555

13.2 Sequential Experimentation 555

13.3 One Step Screening and Optimisation 559

13.4 Evolutionary Operation 560

13.5 Concluding Remarks 562

Bibliography 565

Index 579

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