ISBN-10:
020532178X
ISBN-13:
9780205321780
Pub. Date:
06/07/2000
Publisher:
Pearson Education
Computer-Assisted Research Design and Analysis / Edition 1

Computer-Assisted Research Design and Analysis / Edition 1

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Product Details

ISBN-13: 9780205321780
Publisher: Pearson Education
Publication date: 06/07/2000
Pages: 748
Product dimensions: 7.44(w) x 9.44(h) x 1.35(d)

Table of Contents

Preface xix
Introduction
1(25)
The Nature of Research
1(4)
IVs and DVs
1(1)
What Is an Experiment
2(2)
What if It Isn't an Experiment
4(1)
Relationship between Design and Analysis
4(1)
Monitoring Processes to Aid in Experimental Design
5(1)
Types of Research Designs
5(4)
Randomized-Groups Designs
5(1)
Repeated-Measures Designs
6(1)
One-Way and Factorial Designs
7(1)
Blocking Designs
7(1)
Crossing and Nesting
8(1)
Types of Treatments
9(1)
Qualitative IVs
9(1)
Quantitative IVs
10(1)
Outcome Measures
10(2)
Types of Outcome Measures
10(1)
Number of Outcome Measures
11(1)
Overview of Research Designs
12(3)
Continuous Outcomes
12(1)
Randomized-Groups ANOVA
12(1)
Repeated-Measures ANOVA
12(1)
Analysis of Covariance, ANCOVA
13(1)
Latin-Square ANOVA
14(1)
ANOVA for Screening and Other Incomplete Designs
14(1)
Random-Effects ANOVA
14(1)
Rank-Order Outcomes
14(1)
Discrete Outcomes
15(1)
Log-Linear Models
15(1)
Logistic Regression
15(1)
Time as an Outcome
15(1)
Overview of Issues Encountered in Most Designs
15(4)
Statistical Inference
15(2)
Power
17(1)
Effect Size
17(1)
Parameter Estimates
18(1)
To Test or Not to Test
18(1)
Generalizing Results
19(1)
Computer Assistance
20(2)
Programs for Statistical Analysis
20(1)
Programs for Designing Experiments
21(1)
Organization of the Book
22(1)
Choosing a Design: Some Guidelines
22(2)
Problem Sets
24(2)
Organizing, Describing, and Screening Data
26(40)
Organizing Data for a Computer
26(4)
Discrete, Continuous, and Ordinal Data
26(1)
Randomized-Groups Designs
27(2)
Repeated-Measures Designs
29(1)
Describing Data from One Variable
30(21)
Discrete Variables
31(1)
Central Tendency and Dispersion for Discrete Variables
31(1)
Graphical Methods for Discrete Variables
32(1)
Ordinal Variables
32(1)
Central Tendency and Dispersion for Ordinal Variables
33(1)
Frequency Histogram for Ordinal Variables
34(1)
Continuous Variables
34(1)
Central Tendency and Dispersion for Continuous Variables
35(1)
Parameter Estimates, Confidence Intervals, and Sampling Distributions
36(6)
Normality
42(2)
Outliers
44(4)
Graphical Methods for Continuous Variables
48(3)
Describing Relationships Between Two Variables
51(13)
Both Variables Discrete
51(2)
Both Variables Continuous
53(1)
Correlation
54(2)
Regression
56(1)
One Discrete and One Continuous Variable
57(7)
Problem Sets
64(2)
Basic ANOVA: Logic of Analysis and Tests of Assumptions
66(28)
Introduction
66(1)
Analysis of Variance
67(11)
The General Linear Model
67(1)
Generation of a Data Set
68(2)
The Basic Analysis
70(1)
Basic ANOVA: The Deviation Approach
70(4)
Standard Computational Form
74(2)
Statistical Inference in ANOVA
76(1)
More Later
77(1)
The Regression Approach to ANOVA
78(5)
Bivariate Regression and Basic ANOVA
79(3)
Why Bother
82(1)
Assumptions of Analysis
83(3)
Normality of Sampling Distribution of Means
83(1)
Independence of Errors
84(1)
Homogeneity of Variance
84(1)
Absence of Outliers
85(1)
Other Recurring Issues
86(7)
Effect Size
87(1)
Power
87(1)
Comparisons
87(1)
Orthogonality
88(1)
Coding
88(2)
Missing Values
90(1)
Transformations
90(3)
Overview of Remaining Chapters
93(1)
Problem Sets
93(1)
One-Way Randomized-Groups Analysis of Variance, Fixed-Effects Designs
94(58)
General Purpose and Description
94(1)
Kinds of Research Questions
95(1)
Effect of the IV
95(1)
Specific Comparisons
95(1)
Parameter Estimates
96(1)
Effect Size
96(1)
Power
96(1)
Assumptions and Limitations
96(1)
Theoretical Issues
96(1)
Practical Issues
97(1)
Fundamental Equations
97(11)
Allocation of Cases
97(1)
Partition of Sources of Variance
98(1)
Traditional ANOVA Approach (Three Levels)
98(1)
Regression Approach (Three Levels)
99(4)
Computer Analyses of Small-Sample One-Way Design
103(5)
Some Important Issues
108(29)
Effect Size
108(2)
Power and Sample Size
110(1)
Designing Powerful Studies
110(2)
Estimating Sample Size
112(4)
Power of a Nonsignificant Effect
116(1)
Unequal Sample Sizes
116(2)
Homogeneity of Variance
118(1)
Specific Comparisons
119(1)
Weighting Coefficients for Comparisons
120(1)
Orthogonality of Weighting Coefficients for Comparisons
121(1)
Obtained F for Comparisons
122(1)
Planned Comparisons
122(1)
An Orthogonal Set
123(3)
Welch's Correction for Heterogeneity of Variance
126(1)
Bonferroni Correction
126(1)
Dunnett's Test for Control Group vs. Each Treatment
127(1)
Adjustments for Post Hoc Comparisons
128(1)
Scheffe Post Hoc Test for Complex Comparisons
129(1)
Tukey HSD Test for Pairwise Comparisons
130(2)
Pairwise Comparisons When Assumptions Are Violated
132(1)
Summary of Some Critical Values for Comparisons
132(1)
Trend Analysis
133(3)
Effect Sizes for Comparisons
136(1)
Complete Example of One-Way Anova
137(8)
Evaluation of Assumptions
137(1)
Accuracy of Input, Independence of Errors, Sample Sizes, and Distributions
137(1)
Outliers
137(5)
Homogeneity of Variance
142(1)
Planned Trend Analysis for One-Way Randomized-Groups Design
142(3)
Comparison of Programs
145(5)
SPSS Package
148(1)
SAS System
149(1)
SYSTAT System
149(1)
MINITAB Programs
149(1)
Problem Sets
150(2)
Factorial Randomized-Groups, Fixed-Effects Designs
152(91)
General Purpose and Description
152(1)
Kinds of Research Questions
153(2)
Main Effects of the IVs
153(1)
Effects of Interactions among IVs
153(1)
Specific Comparisons
154(1)
Parameter Estimates
154(1)
Effect Sizes
154(1)
Power
155(1)
Assumptions and Limitations
155(1)
Theoretical Issues
155(1)
Practical Issues
155(1)
Normality of Sampling Distributions
155(1)
Homogeneity of Variance
155(1)
Independence of Errors
156(1)
Absence of Outliers
156(1)
Fundamental Equations
156(20)
Allocation of Cases
156(1)
Partition of Sources of Variance
157(3)
Traditional ANOVA Approach (33)
160(3)
Regression Approach (33)
163(4)
Computer Analyses of Small-Sample Factorial Design
167(9)
Other Types of Randomized-Groups Designs
176(8)
Higher-Order Factorial Designs
177(6)
Factorial Design with a Single Control
183(1)
Some Important Issues
184(40)
Interpreting Interactions
184(1)
Two-Way Designs
184(4)
Higher-Order Factorials
188(2)
Effect Size
190(2)
Power and Sample Size
192(1)
Estimating Sample Size
192(2)
Power of a Nonsignificant Effect
194(1)
Specific Comparisons
195(2)
Types of Comparisons
197(1)
Comparisons on Marginal Means
197(1)
Comparisons on Cell Means
198(1)
Critical F for Planned Comparisons
199(1)
Adjustments for Post Hoc Comparisons
200(1)
Scheffe
200(1)
Tukey
200(1)
Analyzing Interactions
201(1)
Simple Effects
202(10)
Interaction Contrasts
212(5)
Extending Comparisons through the Regression Approach
217(2)
Comparisons in Higher-Order Designs
219(1)
Unequal Sample Sizes
220(4)
Complete Example of Two-Way Randomized-Groups ANOVA
224(13)
Evaluation of Assumptions
225(1)
Sample Sizes, Normality, and Independence of Errors
225(2)
Homogeneity of Variance
227(1)
Outliers
227(1)
Randomized-Groups Analysis of Variance
227(2)
Omnibus Tests
229(1)
Comparisons among Cell Means
229(3)
Interpreting Heterogeneity of Variance
232(5)
Comparison of Programs
237(4)
SPSS Package
237(1)
SAS System
237(4)
SYSTAT System
241(1)
MINITAB Programs
241(1)
Problem Sets
241(2)
Repeated-Measures Designs
243(78)
General Purpose and Description
243(1)
Kinds of Research Questions
244(1)
Effect of the IVs
244(1)
Effect of Interactions among IVs
244(1)
Parameter Estimates
245(1)
Effect Sizes
245(1)
Power
245(1)
Specific Comparisons
245(1)
Assumptions and Limitations
245(4)
Theoretical Issues
245(1)
Practical Issues
246(1)
Normality of Sampling Distributions
246(1)
Homogeneity of Variance
247(1)
Independence of Errors, Additivity, Homogeneity of Covariance, and Sphericity
248(1)
Absence of Outliers
248(1)
Missing Data
249(1)
Fundamental Equations
249(37)
One-Way Repeated-Measures ANOVA
249(1)
Allocation of Cases
249(1)
Partition of Sources of Variance
249(3)
Traditional One-Way Repeated-Measures ANOVA
252(2)
The Regression Approach to One-Way Repeated Measures
254(2)
Computer Analyses of Small-Sample One-Way Repeated-Measures Design
256(7)
Factorial Repeated-Measures Designs
263(1)
Allocation of Cases
264(1)
Partition of Variance
264(2)
Traditional ANOVA for Two-Way Repeated Measures
266(4)
Regression Approach to Factorial Repeated Measures
270(7)
Computer Analyses of Small-Sample Factorial Repeated-Measures Design
277(9)
Types of Repeated-Measures Designs
286(2)
Time as a Variable
286(1)
Simultaneous Repeated Measures
286(1)
Matched Randomized Blocks
287(1)
Some Important Issues
288(20)
Carryover Effects: Control of Extraneous Variables
288(1)
Assumptions of Analysis: Independence of Errors, Sphericity, Additivity, and Compuond Symmetry
289(3)
Power, Sample Size, and Relative Efficiency
292(2)
Effect Size
294(1)
Missing Data
295(1)
Specific Comparisons
296(1)
Tests of Repeated-Measures Main Effects
297(1)
Comparisons for Designs with One Repeated-Measures IV
297(3)
Marginal Comparisons in Factorial Repeated-Measures Designs
300(2)
Tests of Repeated-Measures Interactions: Simple-Effects Analysis
302(1)
Tests of Repeated-Measures Interactions: Interaction Contrasts
302(6)
Complete Example of Two-Way Repeated-Measures Anova
308(8)
Evaluation of Assumptions
308(1)
Normality of Sampling Distributions
308(1)
Homogeneity of Variance
308(1)
Sphericity
309(1)
Absence of Outliers
310(1)
Planned Trend Analysis of Two-Way Repeated-Measures Design
310(6)
Comparison of Programs
316(3)
SPSS Package
316(3)
SAS System
319(1)
SYSTAT System
319(1)
MINITAB Programs
319(1)
Problem Sets
319(2)
Mixed Randomized-Repeated Designs
321(64)
General Purpose and Description
321(1)
Kinds of Research Questions
322(1)
Effects of the IVs
322(1)
Effects of Interactions among IVs
322(1)
Parameter Estimates
322(1)
Effect Sizes
323(1)
Power
323(1)
Specific Comparisons
323(1)
Assumptions and Limitations
323(2)
Theoretical Issues
323(1)
Practical Issues
324(1)
Normality of Sampling Distributions
324(1)
Homogeneity of Variance
324(1)
Independence of Errors, Additivity, and Sphericity
324(1)
Absence of Outliers
325(1)
Missing Data and Unequal Sample Sizes
325(1)
Fundamental Equations
325(21)
Allocation of Cases
325(1)
Partition of Sources of Variance
326(2)
Traditional ANOVA for the Mixed Design
328(3)
Regression Approach to the Mixed Design
331(5)
Computer Analyses of Small-Sample Mixed Design
336(10)
Types of Mixed Designs
346(1)
The Pretest-Posttest Design
346(1)
Expanding Mixed Designs
346(1)
Some Important Issues
347(16)
Comparisons on the Margins
348(1)
The Randomized-Groups Margin
348(3)
The Repeated-Measures Margin
351(1)
Simple Main-Effects Analysis
352(2)
Simple Main Effects for the Randomized-Groups IV
354(1)
Simple Main Effects for the Repeated-Measures IV
355(1)
Simple Comparisons
355(2)
Simple Comparisons for the Randomized-Groups IV
357(1)
Simple Comparisons for the Repeated-Measures IV
358(1)
Interaction Contrasts
358(1)
Comparisons Through the Regression Approach
358(5)
Complete Example of Mixed Randomized-Repeated Anova
363(11)
Evaluation of Assumptions
363(1)
Normality of Sampling Distributions, Missing Data, and Unequal Sample Sizes
363(1)
Homogeneity of Variance
364(1)
Outliers
365(1)
Independence of Errors and Sphericity
366(1)
Three-Way Mixed Randomized-Repeated ANOVA
366(1)
Major Analysis
366(2)
Simple Interaction Comparisons
368(3)
Simple Comparisons
371(3)
Comparison of Programs
374(2)
SPSS Package
374(2)
SAS System
376(1)
SYSTAT System
376(1)
MINITAB Programs
376(1)
Putting It All Together for Factorial Designs
376(7)
Allocation of Cases
376(2)
Assumptions of Analysis
378(1)
Error Terms
378(1)
Setup for Regression
378(4)
Developing Computational Equations from Degrees of Freedom
382(1)
Problem Sets
383(2)
Analysis of Covariance
385(96)
General Purpose and Description
385(3)
Kinds of Research Questions
388(2)
Effect of the IVs
388(1)
Effect of Interactions among IVs
388(1)
Effects of Covariates
388(1)
Parameter Estimates
389(1)
Effect Sizes
389(1)
Power
389(1)
Specific Comparisons
389(1)
Assumptions and Limitations
390(4)
Theoretical Issues
390(1)
Practical Issues
390(1)
Absence of Outliers
391(1)
Absence of Multicollinearity
392(1)
Normality of Sampling Distributions
392(1)
Independence of Errors
393(1)
Homogeneity of Variance
393(1)
Linearity
393(1)
Homogeneity of Regression
393(1)
Reliability of Covariates
394(1)
Fundamental Equations
394(16)
Allocation of Cases to Conditions
395(1)
Partition of Sources of Variance
395(3)
Traditional Approach with Three Levels and One Covariate
398(2)
Computer Analyses of Small-Sample ANCOVA
400(6)
Computer Analysis Using Regression Approach to ANCOVA
406(4)
Types of Designs Using Covariates
410(9)
Randomized-Groups Factorial
410(1)
Repeated Measures
410(1)
Same Covariate(s) for All Cells
410(1)
Traditional Analysis
411(4)
General Linear Model Analysis
415(2)
Varying Covariates(s) over Cells
417(1)
Traditional Analysis
417(1)
General Linear Model Analysis
417(2)
Some Important Issues
419(21)
Multiple Covariates
419(5)
Relationships among Covariates
424(1)
Choosing Covariates
424(2)
Test of Homogeneity of Regression
426(4)
Effect Size
430(2)
Power
432(1)
Adjusted Means
432(2)
Specific Comparisons
434(4)
Alternatives to ANCOVA
438(2)
Complete Examples of Analysis of Covariance
440(9)
One-Way Analysis of Covariance with Five Levels and One Covariate
440(1)
Evaluation of Assumptions
440(1)
Sample Sizes, Missing Data, and Normality of Sampling Distributions
440(1)
Outliers
440(1)
Homogeneity of Variance
441(1)
Linearity
441(2)
Reliability of the Covariate
443(1)
Homogeneity of Regression
443(1)
One-Way Analysis of Covariance
443(1)
Major Analysis
443(2)
Post Hoc Analyses
445(4)
Mixed Randomized-Groups and Repeated-Measures Analysis of Covariance
449(26)
Evaluation of Assumptions
449(1)
Sample Sizes and Missing Data
449(1)
Normality of Sampling Distributions
450(1)
Outliers
451(5)
Independence of Errors
456(1)
Homogeneity of Variance
456(1)
Linearity
456(1)
Reliability of Covariates
457(1)
Multicollinearity
457(1)
Homogeneity of Regression
458(2)
Mixed Randomized-Repeated Analysis of Covariance
460(1)
Omnibus Analyses
460(3)
Post Hoc Analyses
463(12)
Comparison of Programs
475(3)
SPSS Package
475(3)
SAS System
478(1)
SYSTAT System
478(1)
MINITAB Programs
478(1)
Problem Sets
478(3)
Latin-Square Designs
481(70)
General Purpose and Description
481(3)
Kinds of Research Questions
484(1)
Effects of the IVs
484(1)
Interactions among IVs
484(1)
Parameter Estimates
485(1)
Effect Sizes and Power
485(1)
Specific Comparisons
485(1)
Assumptions and Limitations
485(2)
Theoretical Issues
485(1)
Practical Issues
486(1)
Normality of Sampling Distributions
486(1)
Homogeneity of Variance
486(1)
Independence of Errors
486(1)
Absence of Outliers
486(1)
Sphericity
486(1)
Additivity
487(1)
Fundamental Equations
487(13)
Allocation of Cases
488(1)
Partition of Sources of Variance
488(2)
Traditional ANOVA Approach to 333 Latin Square
490(2)
Regression Approach to 333 Latin Square
492(3)
Computer Analyses of Small-Sample Latin-Square Example
495(5)
Types of Latin-Square Designs
500(19)
Replicated Randomized-Groups Designs
500(4)
Replicated Repeated-Measures Designs
504(1)
The Crossover Design
504(2)
The Repeated-Measures Design with a Randomized-Groups IV
506(6)
Comparison of the Two Repeated-Measures Designs with Replications
512(1)
Less Commonly Encountered Designs
513(1)
Partition of Main Effects in a Latin-Square design
513(1)
Greco-Latin Squares
513(1)
Youden Square
514(1)
Digram Balanced Latin-Square, Changeover Designs
514(5)
Some Important Issues
519(10)
Sphericity in Repeated-Measures Designs
519(3)
Choosing a Latin Square
522(2)
Power, Effect Size, and Relative Efficiency
524(2)
Specific Comparisons
526(1)
Randomized-Groups Designs
526(1)
Repeated-Measures Designs
527(1)
Missing Data
528(1)
Complete Examples of Latin-Square ANOVA
529(17)
Complete Example of 444 Randomized-Groups Latin-Square Analysis
529(1)
Evaluation of Assumptions
529(1)
Sample Sizes, Normality, Independence of Errors, and Homogeneity of Variance
530(1)
Outliers
531(1)
Additivity
532(1)
Analysis of 444 Randomized-Groups Latin-Square Design
533(3)
Complete Example of a Repeated-Measures Crossover Design with Multiple Trials
536(1)
Evaluation of Assumptions
537(1)
Sample Sizes, Normality, Independence of Errors, and Homogeneity of Variance
537(2)
Outliers
539(1)
Analysis of Crossover Design
539(7)
Comparison of Programs
546(3)
The SPSS Package
546(1)
The SAS System
547(1)
The SYSTAT System
547(1)
MINITAB Programs
548(1)
Problem Sets
549(2)
Screening and Other Incomplete Designs
551(77)
General Purpose and Description
551(1)
Kinds of Research Questions
552(2)
Effects of the IVs
552(1)
Effects of Interactions among IVs
553(1)
Parameter Estimates
553(1)
Effect Sizes and Power
553(1)
Specific Comparisons
553(1)
Assumptions and Limitations
554(1)
Theoretical Issues
554(1)
Practical Issues
554(1)
Normality of Sampling Distributions
554(1)
Homogeneity of Variance
554(1)
Absence of Outliers
554(1)
Independence of Errors
555(1)
Additivity
555(1)
Fundamental Equations
555(15)
Allocation of Cases
555(1)
Partition of Sources of Variance
556(2)
Regression Approach to a 25 Half-Factorial ANOVA
558(4)
Computer Analyses of the Small-Sample 25 Half-Factorial ANOVA
562(8)
Types of Screening and Other Incomplete Designs
570(29)
Resolution of Incomplete Designs
570(1)
Fractional-Factorial Designs
570(1)
Two-Level Fractional-Factorial Designs
571(3)
Three-Level Fractional-Factorial Designs
574(2)
Plackett-Burman Designs
576(2)
Taguchi Designs
578(7)
Response-Surface Methodology
585(1)
Box-Behnken Design
586(5)
Central Composite Designs
591(1)
Mixture Models
592(1)
Lattice Designs
592(4)
Other Mixture Designs
596(1)
Optimal Designs
596(3)
Some Important Issues
599(9)
Generating Screening and Other Incomplete Designs
599(7)
Choosing among Screening and Other Incomplete Designs
606(1)
Qualitative IVs
606(1)
Quantitative IVs
607(1)
Mixed Qualitative and Quantitative
608(1)
Complete Example of a Central-Composite Design
608(10)
Generating the Design
608(2)
Assumptions and Limitations
610(1)
Normality, Absence of Outliers, and Independence of Errors
610(1)
Homogeneity of Variance
611(1)
Three-Factor Central-Composite Design
611(7)
Comparisons of Programs
618(7)
SAS FACTEX
618(1)
NCSS Design of Experiments
618(6)
MINITAB DOE
624(1)
Problem Sets
625(3)
Analysis of Variance with Random Effects
628(62)
General Purpose and Description
628(1)
Kinds of Research Questions
629(2)
Effect of the IV(s)
629(1)
Effect of Interactions among IVs
630(1)
Specific Comparisons
630(1)
Parameter Estimates
630(1)
Effect Sizes
630(1)
Power
630(1)
Assumptions and Limitations
631(2)
Theoretical Issues
631(1)
Practical Issues
632(1)
Randomized-Groups Designs
632(1)
Repeated-Measures and Mixed (Randomized-Repeated) Designs
633(1)
Fundamental Equations
633(10)
Allocation of Cases
633(1)
Partition of Sources of Variance
634(2)
Traditional ANOVA Approach (One Treatment Factor and One Level of Nesting)
636(2)
Regression Approach (One Treatment Factor and One Level of Nesting)
638(1)
Computer Analyses of Small-Sample Nested Example
638(5)
Types of Designs with Random Effects
643(20)
Nested Designs
644(3)
One-Way Random-Effects Design
647(1)
Factorial Random-Effects Designs
647(3)
Factorial Random Effects ANOVA with Randomized Groups
650(3)
Factorial Random-Effects ANOVA with Repeated Measures
653(2)
Mixed Fixed-Random Designs
655(1)
Mixed Fixed-Random-Effects ANOVA with Randomized Groups
656(2)
Mixed Fixed-Random-Effects ANOVA with Repeated Measures
658(3)
Mixed Fixed-Random-Effects ANOVA with Randomized Groups and Repeated Measures
661(2)
Some Important Issues
663(14)
Error Terms in Random-Effects ANOVA
663(1)
Expected Mean Squares
664(3)
Pooled Error Terms
667(1)
Alternative Strategies to ANOVA
668(5)
Trend Analysis with Unequal Spacing
673(2)
Homogeneity of Covariance
675(1)
Effect Size
676(1)
Complete Example of Random-Effects ANOVA
677(7)
Evaluation of Assumptions
678(1)
Sample Sizes, Normality, and Independence of Errors
678(2)
Homogeneity of Variance and Outliers
680(1)
ANOVA for Doubly Nested Design
680(4)
Comparison of Programs
684(4)
SAS System
684(1)
SPSS Package
685(3)
SYSTAT System
688(1)
MINITAB Programs
688(1)
Problem Sets
688(2)
Appendix A Statistical Tables 690(15)
Table A.1 Critical Values of F Distribution
691(7)
Table A.2 Critical Values of x2 Distribution
698(1)
Table A.3 Critical Values of Studentized Range Statistic Distribution
699(2)
Table A.4 Critical Values of Dunnett's d-Statistic in Comparing Treatment Means with a Control (1-sided test)
701(2)
Table A.5 Coefficients of Orthogonal Polynomials
703(1)
Table A.6 Critical Values of Fmax Distribution
704(1)
Appendix B Research Designs for Complete Examples 705(5)
B.1 Facets in Fly's Eyes
705(1)
B.2 Bonding Strength of Periodontal Dressings
705(1)
B.3 Reaction Time To Identify Figures
706(1)
B.4 Auto Pollution Filter Noise
707(1)
B.5 Wear Testing of Fabric Samples
707(1)
B.6 Odors and Performance
707(1)
B.7 Processing Time for Anova
708(1)
B.8 Nambeware Polishing Times
708(1)
B.9 Chest Deceleration Injuries in Automobile Crashes
709(1)
B.10 Fat Content of Eggs
709(1)
Appendix C Answers to Selected Problems 710(23)
Chapter 1
710(1)
Chapter 2
711(1)
Chapter 3
712(1)
Chapter 4
713(2)
Chapter 5
715(3)
Chapter 6
718(2)
Chapter 7
720(2)
Chapter 8
722(2)
Chapter 9
724(3)
Chapter 10
727(3)
Chapter 11
730(3)
References 733(3)
Index 736

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