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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 Bloc