Design of Experiments: Statistical Principles of Research Design and Analysis / Edition 2

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Overview

Robert Kuehl's DESIGN OF EXPERIMENTS, Second Edition, prepares students to design and analyze experiments that will help them succeed in the real world. Kuehl uses a large array of real data sets from a broad spectrum of scientific and technological fields. This approach provides realistic settings for conducting actual research projects. Next, he emphasizes the importance of developing a treatment design based on a research hypothesis as an initial step, then developing an experimental or observational study design that facilitates efficient data collection. In addition to a consistent focus on research design, Kuehl offers an interpretation for each analysis.

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Editorial Reviews

From the Publisher
The exercises are a great strength of the book. I think the difficulty level, the amount of real data, and the required computer usage are all on target.

I find the book well written, and useful in both a teaching context as well as a consulting one. I find his narrative style in the text, and development of mathematical structures in an appendix at the end of each chapter desirable from a teaching standpoint. The narrative text sets the stage for the mathematical treatment...Kuehl has put the right emphasis on the use of a computer.

Booknews
Presents principles of statistical design and analysis for comparative scientific studies to graduate students in the experimental sciences and applied statistics. Material is applications-oriented, using the results of established theory, and does not include theoretical development. Early chapters emphasize the connection between research hypotheses and treatment designs in randomized designs, and later chapters present developments of experiment designs and their analyses. Examples and exercises are based on actual research studies when possible, demonstrating applications in life and agricultural sciences and in engineering, industrial, and chemistry research. Assumes previous courses in college algebra and an introduction to statistical methods. The author is affiliated with the University of Arizona. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780534368340
  • Publisher: Cengage Learning
  • Publication date: 8/13/1999
  • Series: Statistics Ser.
  • Edition description: REV
  • Edition number: 2
  • Pages: 688
  • Sales rank: 1,234,369
  • Product dimensions: 7.20 (w) x 9.30 (h) x 1.10 (d)

Table of Contents

1. RESEARCH DESIGN PRINCIPLES The Legacy of Sir Ronald A. Fisher / Planning for Research / Experiments, Treatments, and Experimental Units / Research Hypotheses Generate Treatment Designs / Local Control of Experimental Errors / Replication for Valid Experiments / How Many Replications? / Randomization for Valid Inferences / Relative Efficiency of Experiment Designs / From Principles to Practice: A Case Study 2. GETTING STARTED WITH COMPLETELY RANDOMIZED DESIGNS Assembling the Research Design / How to Randomize / Preparation of Data Files for the Analysis / A Statistical Model for the Experiment / Estimation of the Model Parameters with Least Squares / Sums of Squares to Identify Important Sources of Variation / A Treatment Effects Model / Degrees of Freedom / Summaries in the Analysis of Variance Table / Tests of Hypotheses About Linear Models / Significance Testing and Tests of Hypotheses / Standard Errors and Confidence Intervals for Treatment Means / Unequal Replication of the Treatments / How Many Replications of the F Test? / Appendix: Expected Values / Appendix: Expected Mean Squares 3. TREATMENT COMPARISONS Treatment Comparisons Answer Research Questions / Planning Comparisons Among Treatments / Response Curves for Quantitative Treatment Factors / Multiple Comparisons Affect Error Rates / Simultaneous Statistical Inference / Multiple Comparisons with the Best Treatment / Comparison of All Treatments with a Control / Pairwise Comparisons of All Treatments / Summary Comments on Multiple Comparisons / Appendix: Linear Functions of Random Variables 4. DIAGNOSING AGREEMENT BETWEEN THE DATA AND THE MODEL Valid Analysis Depends on Valid Assumptions / Effects of Departures from Assumptions / Residuals Are the Basis of Diagnostic Tools / Looking for Outliers with the Residuals / Variance-Stabilizing Transformations for Data with Known Distributions / Power Transformations to Stabilize Variances / Generalizing the Linear Model / Model Evaluation with Residual-Fitted Spread Plots / Appendix: Data for Example 4.1 5. EXPERIMENTS TO STUDY VARIANCES Random Effects Models for Variances / A Statistical Model for Variance Components / Point Estimates of Variance Components / Interval Estimates for Variance Components / Courses of Action with Negative Variance Estimates / Intraclass Correlation Measures Similarity in a Group / Unequal Numbers of Observations in the Groups / How Many Observations to Study Variances? / Random Subsamples to Procure Data for the Experiment / Using Variance Estimates to Allocate Sampling Efforts / Unequal Numbers of Replications and Subsamples / Appendix: Coefficient Calculations for Expected Mean Squares in Table 5.9 6. FACTORIAL TREATMENT DESIGNS Efficient Experiments with Factorial Treatment Designs / Three Types of Treatment Factor Effects / The Statistical Model for Two Treatment Factors / The Analysis for Two Factors / Using Response Curves for Quantitative Treatment Factors / Three Treatment Factors / Estimation of Error Variance with One Replication / How Many Replications to Test Factor Effects? / Unequal Replication of Treatments / Appendix: Least Squares for Factorial Treatment Designs 7. FACTORIAL TREATMENT DESIGNS: RANDOM AND MIXED MODELS Random Effects for Factorial Treatment Designs / Mixed Models / Nested Factor Designs: A Variation on the Theme / Nested and Crossed Factors Designs / How Many Replications? / Expected Mean Square Rules 8. COMPLETE BLOCK DESIGNS Blocking to Increase Precision / Randomized Complete Block Designs Use One Blocking Criterion / Latin Square Designs Use Two Blocking Criteria / Factorial Experiments in Complete Block Designs / Missing Data in Blocked Designs / Experiments Performed Several Times / Appendix: Selected Latin Squares 9. INCOMPLETE BLOCK DESIGNS: AN INTRODUCTION Incomplete Blocks of Treatments to Reduce Block Size / Balanced Incomplete Block (BIB) Designs / How to Randomize Incomplete Block Designs / Analysis of BIB Designs / Row-Column Designs for Two Blocking Criteria / Reduce Experiment Size with Partially Balanced (PBIB) Designs / Efficiency of Incomplete Block Designs / Appendix: Selected Balanced Incomplete Block Designs / Appendix: Selected Incomplete Latin Square Designs / Appendix: Least Squares Estimates for BIB Designs 10. INCOMPLETE BLOCK DESIGNS: RESOLVABLE AND CYCLIC DESIGNS Resolvable Designs to Help Manage the Experiment / Resolvable Row-Column Designs for Two Blocking Criteria / Cyclic Designs Simplify Design Construction / Choosing Incomplete Block Designs / Appendix: Plans for Cyclic Designs / Appendix: Generating Arrays for a Designs 11. INCOMPLETE BLOCK DESIGNS: FACTORIAL TREATMENT DESIGNS Taking Greater Advantage of Factorial Treatment Designs / 2 to the nth Power Factorials to Evaluate Many Factors / Incomplete Block Designs for 2 to the nth Power Factorials / A General Method to Create Incomplete Blocks / Incomplete Blocks for 3 to the nth Power Factorials / Concluding Remarks / Appendix: Incomplete Block Design Plans for 2 to the nth Power Factorials 12. FRACTIONAL FACTORIAL DESIGNS Reduce Experiment Size with Fractional Treatment Designs / The Half Fraction of the 2 to the nth Power Factorial / Design Resolution Related to Aliases / Analysis of Half Replicate 2^n - 1 Designs / The Quarter Fractions of 2 to the nth Power Factorials / Construction of 2^(n - p) Designs with Resolution III and IV / Genichi Taguchi and Quality Improvement / Concluding Remarks / Appendix: Fractional Factorial Design Plans 13. RESPONSE SURFACE DESIGNS Describe Responses with Equations and Graphs / Identify Important Factors with 2 to the nth Power Factorials / Designs to Estimate Second-Order Response Surfaces / Quadratic Responses Surface Estimation / Response Surface Exploration / Designs for Mixtures of Ingredients / Analysis of Mixture Experiments / Appendix: Least Squares Estimation of Regression Models / Appendix: Location of Coordinates for the Stationary Point / Appendix: Canonical Form of the Quadratic Equation 14. SPLIT-PLOT DESIGNS Plots of Different Size in the Same Experiment / Two Experimental Errors for Two Plot Sizes / The Analysis for Split-Plot Designs / Standard Errors for Treatment Factor Means / Features of the Split-Plot Design / Relative Efficiency of Subplot and Whole-Plot Comparisons / The Split-Split-Plot Design for Three Treatment Factors / The Split-Block Design / Additional Information About Split-Plot Designs 15. REPEATED MEASURES DESIGNS Studies of Time Trends / Relationships Among Repeated Measurements / A Test for the Huynh-Feldt Assumption / A Univariate Analysis of Variance for Repeated Measures / Analysis When Univariate Analysis Assumptions Do Not Hold / Other Experiments with Repeated Measures Properties / Other Models for Correlation Among Repeated Measures / Appendix: The Mauchly Test for Sphericity / Appendix: Degrees of Freedom Adjustments for Repeated Measures Analysis of Variance 16. CROSSOVER DESIGNS Administer All Treatments to Each Experimental Unit / Analysis of Crossover Designs / Balanced Designs for Crossover Studies / Crossover Designs for Two Treatments / Appendix: Coding Data Files for Crossover Studies / Appendix: Treatment Sum of Squares for Balanced Designs 17. ANALYSIS OF COVARIANCE Local Control with a Measured Covariate / Analysis of Covariance for Completely Randomized Block Designs / The Analysis of Covariance for Blocked Experiment Designs / Practical Consequences of Covariance Analysis / REFERENCES / APPENDIX TABLES / ANSWERS TO SELECTED EXERCISES / INDEX

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