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| Research Design Principles The Legacy of Sir Ronald A. Fisher | |
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| Planning for Research | |
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| Experiments, Treatments, and Experimental Units | |
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| Research Hypotheses Generate Treatment Designs | |
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| Local Control of Experimental Errors | |
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| Replication for Valid Experiments | |
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| How Many Replications? | |
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| Randomization for Valid Inferences | |
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| Relative Efficiency of Experiment Designs | |
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| From Principles to Practice: A Case Study | |
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| Getting Started with Completely Randomized Designs Assembling the Research Design | |
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| How to Randomize | |
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| Preparation of Data Files for the Analysis | |
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| A Statistical Model for the Experiment | |
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| Estimation of the Model Parameters with Least Squares | |
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| Sums of Squares to Identify Important Sources of Variation | |
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| A Treatment Effects Model | |
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| Degrees of Freedom | |
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| Summaries in the Analysis of Variance Table | |
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| Tests of Hypotheses About Linear Models | |
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| Significance Testing and Tests of Hypotheses | |
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| Standard Errors and Confidence Intervals for Treatment Means | |
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| Unequal Replication of the Treatments | |
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| How Many Replications of the F Test? | |
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| Appendix: Expected Values | |
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| Appendix: Expected Mean Squares | |
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| Treatment Comparisons Treatment Comparisons Answer Research Questions | |
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| Planning Comparisons Among Treatments | |
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| Response Curves for Quantitative Treatment Factors | |
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| Multiple Comparisons Affect Error Rates | |
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| Simultaneous Statistical Inference | |
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| Multiple Comparisons with the Best Treatment | |
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| Comparison of All Treatments with a Control | |
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| Pairwise Comparisons of All Treatments | |
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| Summary Comments on Multiple Comparisons | |
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| Appendix: Linear Functions of Random Variables | |
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| Diagnosing Agreement Between the Data and the Model Valid Analysis Depends on Valid Assumptions | |
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| Effects of Departures from Assumptions | |
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| Residuals Are the Basis of Diagnostic Tools | |
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| Looking for Outliers with the Residuals | |
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| Variance-Stabilizing Transformations for Data with Known Distributions | |
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| Power Transformations to Stabilize Variances | |
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| Generalizing the Linear Model | |
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| Model Evaluation with Residual-Fitted Spread Plots | |
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| Appendix: Data for Example 4.1 | |
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| Experiments to Study Variances Random Effects Models for Variances | |
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| A Statistical Model for Variance Components | |
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| Point Estimates of Variance Components | |
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| Interval Estimates for Variance Components | |
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| Courses of Action with Negative Variance Estimates | |
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| Intraclass Correlation Measures Similarity in a Group | |
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| Unequal Numbers of Observations in the Groups | |
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| How Many Observations to Study Variances? | |
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| Random Subsamples to Procure Data for the Experiment | |
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| Using Variance Estimates to Allocate Sampling Efforts | |
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| Unequal Numbers of Replications and Subsamples | |
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| Appendix: Coefficient Calculations for Expected Mean Squares in Table 5.9 | |
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| Factorial Treatment Designs Efficient Experiments with Factorial Treatment Designs | |
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| Three Types of Treatment Factor Effects | |
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| The Statistical Model for Two Treatment Factors | |
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| The Analysis for Two Factors | |
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| Using Response Curves for Quantitative Treatment Factors | |
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| Three Treatment Factors | |
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| Estimation of Error Variance with One Replication | |
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| How Many Replications to Test Factor Effects? | |
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| Unequal Replication of Treatments | |
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| Appendix: Least Squares for Factorial Treatment Designs | |
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| Factorial Treatment Designs: Random and Mixed Models Random Effects for Factorial Treatment Designs | |
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| Mixed Models | |
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| Nested Factor Designs: A Variation on the Theme | |
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| Nested and Crossed Factors Designs | |
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| How Many Replications? | |
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| Expected Mean Square Rules | |
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| Complete Block Designs Blocking to Increase Precision | |
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| Randomized Complete Block Designs Use One Blocking Criterion | |
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| Latin Square Designs Use Two Blocking Criteria | |
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| Factorial Experiments in Complete Block Designs | |
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| Missing Data in Blocked Designs | |
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| Experiments Performed Several Times | |
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| Appendix: Selected Latin Squares | |
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| Incomplete Block Designs: An Introduction Incomplete Blocks of Treatments to Reduce Block Size | |
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| Balanced Incomplete Block (BIB) Designs | |
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| How to Randomize Incomplete Block Designs | |
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| Analysis of BIB Designs | |
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| Row-Column Designs for Two Blocking Criteria | |
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| Reduce Experiment Size with Partially Balanced (PBIB) Designs | |
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| Efficiency of Incomplete Block Designs | |
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| Appendix: Selected Balanced Incomplete Block Designs | |
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| Appendix: Selected Incomplete Latin Square Designs | |
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| Appendix: Least Squares Estimates for BIB Designs | |
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| Incomplete Block Designs: Resolvable and Cyclic Designs Resolvable Designs to Help Manage the Experiment | |
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| Resolvable Row-Column Designs for Two Blocking Criteria | |
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| Cyclic Designs Simplify Design Construction | |
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| Choosing Incomplete Block Designs | |
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| Appendix: Plans for Cyclic Designs | |
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| Appendix: Generating Arrays for a Designs | |
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| Incomplete Block Designs: Factorial Treatment Designs Taking Greater Advantage of Factorial Treatment Designs | |
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| 2 to the nth Power Factorials to Evaluate Many Factors | |
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| Incomplete Block Designs for 2 to the nth Power Factorials | |
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| A General Method to Create Incomplete Blocks | |
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| Incomplete Blocks for 3 to the nth Power Factorials | |
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| Concluding Remarks | |
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| Appendix: Incomplete Block Design Plans for 2 to the nth Power Factorials | |
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| Fractional Factorial Designs Reduce Experiment Size with Fractional Treatment Designs | |
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| The Half Fraction of the 2 to the nth Power Factorial | |
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| Design Resolution Related to Aliases | |
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| Analysis of Half Replicate 2^n - 1 Designs | |
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| The Quarter Fractions of 2 to the nth Power Factorials | |
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| Construction of 2^(n - p) Designs with Resolution III and IV | |
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| Genichi Taguchi and Quality Improvement | |
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| Concluding Remarks | |
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| Appendix: Fractional Factorial Design Plans | |
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| Response Surface Designs Describe Responses with Equations and Graphs | |
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| Identify Important Factors with 2 to the nth Power Factorials | |
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| Designs to Estimate Second-Order Response Surfaces | |
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| Quadratic Responses Surface Estimation | |
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| Response Surface Exploration | |
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| Designs for Mixtures of Ingredients | |
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| Analysis of Mixture Experiments | |
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| Appendix: Least Squares Estimation of Regression Models | |
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| Appendix: Location of Coordinates for the Stationary Point | |
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| Appendix: Canonical Form of the Quadratic Equation | |
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| Split-Plot Designs Plots of Different Size in the Same Experiment | |
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| Two Experimental Errors for Two Plot Sizes | |
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| The Analysis for Split-Plot Designs | |
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| Standard Errors for Treatment Factor Means | |
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| Features of the Split-Plot Design | |
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| Relative Efficiency of Subplot and Whole-Plot Comparisons | |
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| The Split-Split-Plot Design for Three Treatment Factors | |
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| The Split-Block Design | |
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| Additional Information About Split-Plot Designs | |
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| Repeated Measures Designs Studies of Time Trends | |
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| Relationships Among Repeated Measurements | |
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| A Test for the Huynh-Feldt Assumption | |
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| A Univariate Analysis of Variance for Repeated Measures | |
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| Analysis When Univariate Analysis Assumptions Do Not Hold | |
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| Other Experiments with Repeated Measures Properties | |
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| Other Models for Correlation Among Repeated Measures | |
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| Appendix: The Mauchly Test for Sphericity | |
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| Appendix: Degrees of Freedom Adjustments for Repeated Measures Analysis of Variance | |
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| Crossover Designs Administer All Treatments to Each Experimental Unit | |
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| Analysis of Crossover Designs | |
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| Balanced Designs for Crossover Studies | |
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| Crossover Designs for Two Treatments | |
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| Appendix: Coding Data Files for Crossover Studies | |
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| Appendix: Treatment Sum of Squares for Balanced Designs | |
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| Analysis Of Covariance Local Control with a Measured Covariate | |
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| Analysis of Covariance for Completely Randomized Block Designs | |
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| The Analysis of Covariance for Blocked Experiment Designs | |
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| Practical Consequences of Covariance Analysis | |
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| References | |
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| Appendix Tables | |
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| Answers to Selected Exercises | |
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| Index | |