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9780534368340

Design of Experiments Statistical Principles of Research Design and Analysis

by
  • ISBN13:

    9780534368340

  • ISBN10:

    0534368344

  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 1999-08-13
  • Publisher: Cengage Learning

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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

Research Design Principles
1(36)
The Legacy of Sir Ronald A. Fisher
1(1)
Planning for Research
2(1)
Experiments, Treatments, and Experimental Units
3(2)
Research Hypotheses Generate Treatment Designs
5(3)
Local Control of Experimental Errors
8(8)
Replication for Valid Experiments
16(2)
How Many Replications?
18(2)
Randomization for Valid Inferences
20(5)
Relative Efficiency of Experiment Designs
25(1)
From Principles to Practice: A Case Study
26(11)
Getting Started with Completely Randomized Designs
37(36)
Assembling the Research Design
37(2)
How to Randomize
39(2)
Preparation of Data Files for the Analysis
41(1)
A Statistical Model for the Experiment
42(5)
Estimation of the Model Parameters with Least Squares
47(3)
Sums of Squares to Identify Important Sources of Variation
50(3)
A Treatment Effects Model
53(1)
Degrees of Freedom
54(1)
Summaries in the Analysis of Variance Table
55(1)
Tests of Hypotheses About Linear Models
56(2)
Significance Testing and Tests of Hypotheses
58(1)
Standard Errors and Confidence Intervals for Treatment Means
59(1)
Unequal Replication of the Treatments
60(3)
How Many Replications for the F Test?
63(10)
Appendix: Expected Values
70(1)
Appendix: Expected Mean Squares
71(2)
Treatment Comparisons
73(50)
Treatment Comparisons Answer Research Questions
73(1)
Planning Comparisons Among Treatments
74(9)
Response Curves for Quantitative Treatment Factors
83(8)
Multiple Comparisons Affect Error Rates
91(3)
Simultaneous Statistical Inference
94(4)
Multiple Comparisons with the Best Treatment
98(6)
Comparison of All Treatments with a Control
104(3)
Pairwise Comparison of All Treatments
107(8)
Summary Comments on Multiple Comparisons
115(8)
Appendix: Linear Functions of Random Variables
121(2)
Diagnosing Agreement Between the Data and the Model
123(25)
Valid Analysis Depends on Valid Assumptions
123(1)
Effects of Departures from Assumptions
123(1)
Residuals Are the Basis of Diagnostic Tools
124(7)
Looking for Outliers with the Residuals
131(2)
Variance-Stabilizing Transformations for Data with Known Distributions
133(2)
Power Transformations to Stabilize Variances
135(5)
Generalizing the Linear Model
140(1)
Model Evaluation with Residual-Fitted Spread Plots
141(7)
Appendix: Data for Example 4.1
147(1)
Experiments to Study Variances
148(27)
Random Effects Models for Variances
148(3)
A Statistical Model for Variance Components
151(1)
Point Estimates of Variance Components
152(1)
Interval Estimates for Variance Components
153(2)
Courses of Action with Negative Variance Estimates
155(1)
Intraclass Correlation Measures Similarity in a Group
155(2)
Unequal Numbers of Observations in the Groups
157(1)
How Many Observations to Study Variances?
158(1)
Random Subsamples to Procure Data for the Experiment
159(4)
Using Variance Estimates to Allocate Sampling Efforts
163(1)
Unequal Numbers of Replications and Subsamples
164(11)
Appendix: Coefficient Calculations for Expected Mean Squares in Table 5.9
174(1)
Factorial Treatment Designs
175(57)
Efficient Experiments with Factorial Treatment Designs
175(2)
Three Types of Treatment Factor Effects
177(4)
The Statistical Model for Two Treatment Factors
181(2)
The Analysis for Two Factors
183(7)
Using Response Curves for Quantitative Treatment Factors
190(9)
Three Treatment Factors
199(6)
Estimation of Error Variance with One Replication
205(3)
How Many Replication to Test Factor Effects?
208(1)
Unequal Replication of Treatments
208(24)
Appendix: Least Squares for Factorial Treatment Designs
225(7)
Factorial Treatment Designs: Random and Mixed Models
232(31)
Random Effects for Factorial Treatment Designs
232(5)
Mixed Models
237(6)
Nested Factor Designs: A Variation on the Theme
243(8)
Nested and Crossed Factors Designs
251(4)
How Many Replications?
255(1)
Expected Mean Square Rules
255(8)
Complete Block Designs
263(47)
Blocking to Increase Precision
263(1)
Randomized Complete Block Designs Use One Blocking Criterion
264(11)
Latin Square Designs Use Two Blocking Criteria
275(14)
Factorial Experiments in Complete Block Designs
289(2)
Missing Data in Blocked Designs
291(1)
Experiments Performed Several Times
292(18)
Appendix: Selected Latin Squares
307(3)
Incomplete Block Designs: An Introduction
310(29)
Incomplete Blocks of Treatments to Reduce Block Size
310(2)
Balanced Incomplete Block (BIB) Designs
312(1)
How to Randomize Incomplete Block Designs
313(2)
Analysis of BIB Designs
315(5)
Row--Column Designs for Two Blocking Criteria
320(2)
Reduce Experiment Size with Partially Balanced (PBIB) Designs
322(3)
Efficiency of Incomplete Block Design
325(14)
Appendix: Selected Balanced Incomplete Block Designs
330(2)
Appendix: Selected Incomplete Latin Square Designs
332(4)
Appendix: Least Squares Estimates for BIB Designs
336(3)
Incomplete Block Designs: Resolvable and Cyclic Designs
339(23)
Resolvable Designs to Help Manage the Experiment
339(3)
Resolvable Row--Column Designs for Two Blocking Criteria
342(3)
Cyclic Designs Simplify Design Construction
345(7)
Choosing Incomplete Block Designs
352(10)
Appendix: Plans for Cyclic Designs
360(1)
Appendix: Generating Arrays for α Designs
360(2)
Incomplete Block Designs: Factorial Treatment Designs
362(29)
Taking Greater Advantage of Factorial Treatment Designs
362(1)
2n Factorials to Evaluate Many Factors
363(6)
Incomplete Block Designs for 2n Factorials
369(9)
A General Method to Create Incomplete Blocks
378(5)
Incomplete Block Designs for 3n Factorials
383(4)
Concluding Remarks
387(4)
Appendix: Incomplete Block Design Plans for 2n Factorials
390(1)
Fractional Factorial Designs
391(32)
Reduce Experiment Size with Fractional Treatment Designs
391(2)
The Half Fraction of the 2n Factorial
393(5)
Design Resolution Related to Aliases
398(1)
Analysis of Half Replicate 2n--1 Designs
399(7)
The Quarter Fractions of 2n Factorials
406(3)
Construction of 2n--p Designs with Resolution III and IV
409(4)
Genichi Taguchi and Quality Improvement
413(2)
Concluding Remarks
415(8)
Appendix: Fractional Factorial Design Plans
421(2)
Response Surface Designs
423(46)
Describe Responses with Equations and Graphs
423(3)
Identify Important Factors with 2n Factorials
426(5)
Designs to Estimate Second-Order Response Surfaces
431(9)
Quadratic Response Surface Estimation
440(4)
Response Surface Exploration
444(5)
Designs for Mixtures of Ingredients
449(4)
Analysis for Mixtures Experiments
453(16)
Appendix: Least Squares Estimation of Regression Models
463(3)
Appendix: Location of Coordinates for the Stationary Point
466(1)
Appendix: Canonical Form of the Quadratic Equation
467(2)
Split-Plot Designs
469(23)
Plots of Different Size in the Same Experiment
469(3)
Two Experimental Errors for Two Plot Sizes
472(1)
The Analysis for Split-Plot Designs
473(5)
Standard Errors for Treatment Factor Means
478(2)
Features of the Split-Plot Design
480(1)
Relative Efficiency of Subplot and Whole-Plot Comparisons
481(2)
The Split-Split-Plot Design for Three Treatment Factors
483(1)
The Split-Block Design
483(3)
Additional Information About Split-Plot Designs
486(6)
Repeated Measures Designs
492(28)
Studies of Time Trends
492(3)
Relationships Among Repeated Measurements
495(3)
A Test for the Huynh--Feldt Assumption
498(1)
A Univariate Analysis of Variance for Repeated Measures
499(3)
Analysis When Univariate Analysis Assumptions Do Not Hold
502(8)
Other Experiments with Repeated Measures Properties
510(1)
Other Models for Correlation Among Repeated Measures
511(9)
Appendix: The Mauchly Test for Sphericity
518(1)
Appendix: Degrees of Freedom Adjustments for Repeated Measures Analysis of Variance
519(1)
Crossover Designs
520(30)
Administer All Treatments to Each Experimental Unit
520(4)
Analysis of Crossover Designs
524(6)
Balanced Designs for Crossover Studies
530(6)
Crossover Designs for Two Treatments
536(14)
Appendix: Coding Data Files for Crossover Studies
545(2)
Appendix: Treatment Sum of Squares for Balanced Designs
547(3)
Analysis of Covariance
550(26)
Local Control with a Measured Covariate
550(3)
Analysis of Covariance for Completely Randomized Designs
553(12)
The Analysis of Covariance for Blocked Experiment Designs
565(5)
Practical Consequences of Covariance Analysis
570(6)
References 576(11)
Appendix Tables 587(46)
Answers to Selected Exercises 633(28)
Index 661

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