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Author:   Charles R. Hicks (Professor Emeritus of Statistics and Education, Professor Emeritus of Statistics and Education, Purdue University) ,  Kenneth V. Turner (Professor of Mathematics, Professor of Mathematics, Anderson University) ,  Kenneth V. Turner (Professor of Mathematics, Anderson University, USA)
Publisher:   Oxford University Press Inc
Imprint:   Oxford University Press Inc
Edition:   5th Revised edition
Dimensions:   Width: 23.90cm , Height: 3.10cm , Length: 18.80cm
Weight:   1.148kg
ISBN:  

9780195122732

ISBN 10:   0195122739
Pages:   576
Publication Date:   01 July 1999
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Preface 1.: The Experiment, the Design, and the Analysis 1.1: Introduction to Experimental Design 1.2: The Experiment 1.3: The Design 1.4: The Analysis 1.5: Examples 1.6: Summary in Outline 1.7: Further Reading Problems 2.: Review of Statistical Inference 2.1: Introduction 2.2: Estimation 2.3: Tests of Hypothesis 2.4: The Operating Characteristic Curve 2.5: How Large a Sample? 2.6: Application to Tests on Variances 2.7: Application to Tests on Means 2.8: Assessing Normality 2.9: Applications to Tests on Proportions 2.10: Analysis of Experiments with SAS 2.11: Further Reading Problems 3.: Single-Factor Experiments with No Restrictions on Randomization 3.1: Introduction 3.2: Analysis of Variance Rationale 3.3: After ANOVA--What? 3.4: Tests on Means 3.5: Confidence Limits on Means 3.6: Components of Variance 3.7: Checking the Model 3.8: SAS Programs for ANOVA and Tests after ANOVA 3.9: Summary 3.10: Further Reading Problems 4.: Single-Factor Experiments: Randomized Block and Latin Square Designs 4.1: Introduction 4.2: Randomized Complete Block Design 4.3: ANOVA Rationale 4.4: Missing Values 4.5: Latin Squares 4.6: Interpretations 4.7: Assessing the Model 4.8: Graeco-Latin Squares 4.9: Extensions 4.10: SAS Programs for Randomized Blocks and Latin Squares 4.11: Summary 4.12: Further Reading Problems 5.: Factorial Experiments 5.1: Introduction 5.2: Factorial Experiments: An Example 5.3: Interpretations 5.4: The Model and Its Assessment 5.5: ANOVA Rationale 5.6: One Observation Per Treatment 5.7: SAS Programs for Factorial Experiments 5.8: Summary 5.9: Further Reading Problems 6.: Fixed, Random, and Mixed Models 6.1: Introduction 6.2: Single-Factor Models 6.3: Two-Factor Models 6.4: EMS Rules 6.5: EMS Derivations 6.6: The Pseudo-F Test 6.7: Expected Mean Squares Via Statistical Computing Packages 6.8: Remarks 6.9: Repeatability and Reproducibility for a Measurement System 6.10: SAS Problems for Random and Mixed Models 6.11: Further Reading Problems 7.: Nested and Nested-Factorial Experiments 7.1: Introduction 7.2: Nested Experiments 7.3: ANOVA Rationale 7.4: Nested-Factorial Experiments 7.5: Repeated-Measures Design and Nested-Factorial Experiments 7.6: SAS Programs for Nested and Nested-Factorial Experiments 7.7: Summary Further Reading Problems 8.: Experiments of Two or More Factors: Restrictions on Randomization 8.1: Introduction 8.2: Factorial Experiment in a Randomized Block Design 8.3: Factorial Experiment in a Latin Square Design 8.4: Remarks 8.5: SAS Programs 8.6: Summary Problems 9. 2f Factorial Experiments: 9.1: Introduction 9.2: 2 Squared Factorial 9.3: 2 Cubed Factorial 9.4: 2f Remarks 9.5: The Yates Method 9.6: Analysis of 2f Factorials When n=1 9.7 Some Commments about Computer Use: 9.8: Summary 9.9: Further Reading Problems 10.: 3f Factorial Experiments 10.1: Introduction 10.2: 3 Squared Factorial 10.3: 3 Cubed Factorial 10.4: Computer Programs 10.5: Summary Problems 11.: Factorial Experiment: Split-Plot Design 11.1: Introduction 11.2: A Split-Plot Design 11.3: A Split-Split-Plot Design 11.4: Using SAS to Analyze a Split-Plot Experiment 11.5: Summary 11.6: Further Reading Problems 12.: Factorial Experiment: Confounding in Blocks 12.1: Introduction 12.2: Confounding Systems 12.3: Block Confounding, No Replication 12.4: Block Confounding with Replication 12.5: Confounding in 3F Factorials 12.6: SAS Progrms 12.7: Summary 12.8: Further Reading Problems 13: Fractional Replication 13.1: Introduction 13.2: Aliases 13.3: 2f Fractional Replications 13.4: Plackett-Burman Designs 13.5: Design Resolution 13.6: 3f-k Fractional Factorials 13.7: SAS Programs 13.8: Summary 13.9: Further Reading Problems 14.: The Taguchi Approach to the Design of Experiments 14.1: Introduction 14.2: The L4 (2 Cubed) Orthogonal Array 14.3: Outer Arrays 14.4: Signal-To-Noise Ratio 14.5: The L8 (2 7) Orthogonal Array 14.6: The L16 (2 15) Orthogonal Array 14.7: The L9 (3 4) Orthogonal Array 14.8: Some Other Taguchi Designs 14.9: Summary 14.10: Further Reading Problems 15: Regression 15.1: Introduction 15.2: Linear Regression 15.3: Curvilinear Regression 15.4: Orthogonal Polynomials 15.5: Multiple Regression 15.6: Summary 15.7: Further Reading Problems 16.: Miscellaneous Topics 16.1: Introduction 16.2: Covariance Analysis 16.3: Response Surface Experimentation 16.4: Evolutionary Operation (EVOP) 16.5: Analysis of Attribute Data 16.6: Randomized Incomplete Blocks: Restriction On Experimentation 16.7: Youden Squares 16.8: Further Reading Problems Summary and Special Problems Glossary of Terms References Statistical Tables Table A: Areas Under the Normal Curve Table B: Student's t Distribution Table C: Cumulative Chi-Square Distribution Table D: Cumulative F Distribution Table E.1: Upper 5% of Studentized Range q Table E.2: Upper 1% of Studentized Range q Table F: Coefficients of Orthogonal Polynomials Answers to Selected Problems Index

Reviews

An excellent presentation of the basic concepts of experimental design. It uses many numerical examples with 'real' data. It is clearly written and at the appropriate level for my students. --Noel Artiles-Leon, University of Puerto Rico

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