Design of Experiments Statistical Principles of Research Design and Analysis

ISBN-10: 0534368344
ISBN-13: 9780534368340
Edition: 2nd 2000 (Revised)
Authors: Robert O. Kuehl
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Description: 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  More...

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Book details

List price: $369.95
Edition: 2nd
Copyright year: 2000
Publisher: Brooks/Cole
Publication date: 8/13/1999
Binding: Hardcover
Pages: 688
Size: 7.75" wide x 9.75" long x 1.50" tall
Weight: 2.640
Language: English

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.

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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