Title:
Design of experiments : statistical principles of research design and analysis
Personal Author:
Edition:
2nd ed.
Publication Information:
Pacific Grove : Duxbury Thomson Learning, 2000
ISBN:
9780534368340
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000004848069 | Q182.3 K835 2000 | Open Access Book | Book | Searching... |
Searching... | 30000004506907 | Q182.3 K835 2000 | Open Access Book | Book | Searching... |
Searching... | 30000004500645 | Q182.3 K835 2000 | Open Access Book | Book | Searching... |
Searching... | 30000004500637 | Q182.3 K835 2000 | Open Access Book | Book | Searching... |
Searching... | 30000005032101 | Q182.3 K835 2000 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
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.
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 |