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The Statistical Sleuth A Course in Methods of Data Analysis

by ;
Edition:
2nd
ISBN13:

9780534386702

ISBN10:
0534386709
Format:
Hardcover
Pub. Date:
10/5/2001
Publisher(s):
Duxbury Press

Questions About This Book?

What version or edition is this?
This is the 2nd edition with a publication date of 10/5/2001.
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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.
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  • The Statistical Sleuth: A Course in Methods of Data Analysis
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Summary

STATISTICAL SLEUTH is an innovative treatment of general statistical methods, taking full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package. In "The American Statistician" (February 2000, Vol. 54, No. 1), George Cobb commented, "What is new and different about Ramsey and Schafer's book, what makes it a 'larger contribution,' is that it gives much more prominence to modeling and interpretation of the sort that goes beyond the routine patterns." His students did "substantially better" on term papers based on the analysis of data. In the book, the focus is on a serious analysis of real case studies; on strategies and tools of modern statistical data analysis; on the interplay of statistics and scientific learning; and on the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get students excited about statistics.

Table of Contents

Drawing Statistical Conclusions
1(27)
Case Studies
2(3)
Motivation and Creativity--A Randomized Experiment
2(2)
Sex Discrimination in Employment--An Observational Study
4(1)
Statistical Inference and Study Design
5(5)
Causal Inference
5(2)
Inference to Populations
7(1)
Statistical Inference and Chance Mechanisms
8(2)
Measuring Uncertainty in Randomized Experiments
10(4)
A Probability Model for Randomized Experiments
10(1)
A Test for Treatment Effect in the Creativity Study
11(3)
Measuring Uncertainty in Observational Studies
14(2)
A Probability Model for Random Sampling
14(1)
Testing for a Difference in the Sex Discrimination Study
15(1)
Related Issues
16(6)
Graphical Methods
16(4)
Standard Statistical Terminology
20(1)
Randomization of Experimental Units to Treatments
21(1)
Selecting a Simple Random Sample from a Population
21(1)
On Being Representative
22(1)
Summary
22(1)
Exercises
22(6)
Conceptual Exercises
22(2)
Computational Exercises
24(2)
Answers to Conceptual Exercises
26(2)
Inference Using t Distributions
28(28)
Case Studies
29(2)
Bumpus's Data on Natural Selection--An Observational Study
29(1)
Anatomical Abnormalities Associated with Schizophrenia--An Observational Study
30(1)
One-Sample t-Tools and the Paired t-Test
31(6)
The Sampling Distribution of a Sample Average
31(2)
The Standard Error of an Average in Random Sampling
33(1)
The t-Ratio Based on a Sample Average
34(1)
Unraveling the t-Ratio
35(2)
A t-Ratio for Two-sample Inference
37(7)
Sampling Distribution of the Difference Between Two Independent Sample Averages
38(1)
Standard Error for the Difference of Two Averages
39(1)
Confidence Interval for the Difference Between Population Means
40(2)
Testing a Hypothesis About the Difference Between Means
42(1)
The Mechanics of p - Value Computation
43(1)
Inferences in a Two-Treatment Randomized Experiment
44(2)
Approximate Uncertainty Measures for Randomized Experiments
45(1)
Related Issues
46(3)
Interpretation of p - Values
46(1)
An Example of Confidence Intervals
47(2)
The Rejection Region Approach to Hypothesis Testing
49(1)
Summary
49(1)
Exercises
50(6)
Conceptual Exercises
50(1)
Computational Exercises
51(1)
Data Problems
52(2)
Answers to Conceptual Exercises
54(2)
A Closer Look at Assumptions
56(29)
Case Studies
57(3)
Cloud Seeding to Increase Rainfall--A Randomized Experiment
57(1)
Effects of Agent Orange on Troops in Vietnam--An Observational Study
58(2)
Robustness of the Two-Sample t-Tools
60(4)
The Meaning of Robustness
60(1)
Robustness Against Departures from Normality
60(2)
Robustness Against Differing Standard Deviations
62(1)
Robustness Against Departures from Independence
62(2)
Resistance of the Two-Sample t-Tools
64(1)
Outliers and Resistance
64(1)
Resistance of t-Tools
65(1)
Practical Strategies for the Two-Sample Problem
65(3)
Transformations of the Data
68(5)
The Logarithmic Transformation
68(1)
Interpretation After a Log Transformation
69(3)
Other Transformations for Positive Measurements
72(1)
Related Issues
73(1)
Prefer Graphical Methods Over Formal Tests for Model Adequacy
73(1)
Robustness and Transformation for Paired t-Tools
73(1)
Example--Schizophrenia
74(1)
Summary
74(1)
Exercises
75(10)
Conceptual Exercises
75(2)
Computational Exercises
77(4)
Data Problems
81(1)
Answers to Conceptual Exercises
82(3)
Alternatives to the t-Tools
85(28)
Case Studies
86(3)
Space Shuttle O-Ring Failures--An Observational Study
86(1)
Cognitive Load Theory in Teaching--A Randomized Experiment
87(2)
The Rank-Sum Test
89(6)
The Rank Transformation
89(1)
The Rank-Sum Statistic
90(1)
Finding a p - Value by Normal Approximation
90(3)
A Confidence Interval Based on the Rank-Sum Test
93(2)
Other Alternatives for Two Independent Samples
95(4)
Permutation Tests
95(2)
The Welch t-Test for Comparing Two Normal Populations with Unequal Spreads
97(2)
Alternatives for Paired Data
99(2)
The Sign Test
99(1)
The Wilcoxon Signed-Rank Test
100(1)
Related Issues
101(3)
Practical and Statistical Significance
101(1)
The Presentation of Statistical Findings
102(1)
Levene's Test for Equality of Two Variances
102(1)
Survey Sampling
103(1)
Summary
104(1)
Exercises
105(8)
Conceptual Exercises
105(1)
Computational Exercises
106(2)
Data Problems
108(3)
Answers to Conceptual Exercises
111(2)
Comparisons Among Several Samples
113(36)
Case Studies
114(5)
Diet Restriction and Longevity--A Randomized Experiment
114(3)
The Spock Conspiracy Trial--An Observational Study
117(2)
Comparing Any Two of the Several Means
119(2)
An Ideal Model for Several-Sample Comparisons
119(1)
The Pooled Estimate of the Standard Deviation
120(1)
t-Tests and Confidence Intervals for Differences of Means
120(1)
The One-Way Analysis of Variance F-Test
121(6)
The Extra-Sum-of Squares Principle
121(5)
The Analysis of Variance Table for One-Way Classification
126(1)
More Applications of the Extra-Sum-of-Squares F-Test
127(3)
Example: Testing Equality in a Subset of Groups
127(2)
Summary of ANOVA Tests Involving More Than Two Models
129(1)
Robustness and Model Checking
130(3)
Robustness to Assumptions
130(1)
Diagnostics Using Residuals
131(2)
Related Issues
133(7)
Further Illustration of the Different Sources of Variability
133(3)
Kruskal-Wallis Nonparametric Analysis of Variance
136(1)
Random Effects
136(2)
Separate Confidence Intervals and Significant Differences
138(2)
Summary
140(1)
Exercises
141(8)
Conceptual Exercises
141(1)
Computational Exercises
141(5)
Data Problems
146(1)
Answers to Conceptual Exercises
147(2)
Linear Combinations and Multiple Comparisons of Means
149(25)
Case Studies
150(2)
Discrimination Against the Handicapped-A Randomized Experiment
150(1)
Preexisting Preferences of Fish-A Randomized Experiment
151(1)
Inferences About Linear Combinations of Group Means
152(7)
Linear Combinations of Group Means
152(2)
Inferences About Linear Combinations of Group Means
154(2)
Specific Linear Combinations
156(3)
Simultaneous Inferences
159(2)
Some Multiple Comparison Procedures
161(4)
Tukey-Kramer Procedure and the Studentized Range Distributions
161(1)
Scheffe's Procedure
162(1)
Other Multiple Comparisons Procedures
162(1)
Multiple Comparisons in the Handicap Study
163(1)
Choosing a Multiple Comparisons Procedure
164(1)
Related Issues
165(3)
Fishing Expeditions with Many Response Measurements
165(1)
Example of a Hypothesis Based on How the Data Turned Out
165(2)
Is Choosing a Transformation a Form of Data Snooping?
167(1)
Summary
168(1)
Exercises
168(6)
Conceptual Exercises
168(1)
Computational Exercises
169(2)
Data Problems
171(1)
Answers to Conceptual Exercises
172(2)
Simple Linear Regression: A Model for the Mean
174(32)
Case Studies
175(3)
The Big Bang--An Observational Study
175(2)
Meat Processing and pH--A Randomized Experiment
177(1)
The Simple Linear Regression Model
178(3)
Regression Terminology
178(1)
Interpolation and Extrapolation
179(2)
Least Squares Regression Estimation
181(5)
Fitted Values and Residuals
181(1)
Least Squares Estimators
182(1)
Sampling Distributions of the Least Squares Estimators
182(1)
Estimation of σ from Residuals
182(2)
Standard Errors
184(2)
Inferential Tools
186(6)
Tests and Confidence Intervals for Slope and Intercept
186(1)
Describing the Distribution of the Response Variable at Some Value of the Explanatory Variable
186(3)
Prediction of a Future Response
189(1)
Calibration: Estimating the X that Results in Y = Y0
190(2)
Related Issues
192(3)
Historical Notes About Regression
192(1)
Differing Terminology
193(1)
Causation
194(1)
Correlation
194(1)
Planning an Experiment: Replication
195(1)
Summary
195(1)
Exercises
196(10)
Conceptual Exercises
196(1)
Computational Exercises
196(6)
Data Problems
202(3)
Answers to Conceptual Exercises
205(1)
A Closer Look at Assumptions for Simple Linear Regression
206(29)
Case Studies
207(3)
Island Area and Number of Species--An Observational Study
207(1)
Breakdown Times for Insulating Fluid Under Different Voltages--A Controlled Experiment
208(2)
Robustness of Least Squares Inferences
210(2)
Graphical Tools for Model Assessment
212(3)
Scatterplot of the Response Variable Versus the Explanatory Variable
212(2)
Scatterplots of Residuals Versus Fitted Values
214(1)
Interpretation After Log Transformations
215(2)
Assessment of Fit Using the Analysis of Variance
217(4)
Three Models for the Population Means
217(1)
The Analysis of Variance Table Associated with Simple Regression
217(2)
The Lack-of Fit F-Test
219(1)
A Composite Analysis of Variance Table
219(2)
Related Issues
221(4)
R-Squared: The Proportion of Variation Explained
221(1)
Simple Linear Regression or One-Way Analysis of Variance?
222(1)
Other Residual Plots for Special Situations
223(1)
Planning an Experiment: Balance
224(1)
Summary
225(1)
Exercises
226(9)
Conceptual Exercises
226(1)
Computational Exercises
227(3)
Data Problems
230(3)
Answers to Conceptual Exercises
233(2)
Multiple Regression
235(32)
Case Studies
236(4)
Effects of Light on Meadowfoam Flowering--A Randomized Experiment
236(1)
Why Do Some Mammals Have Large Brains for Their Size?--An Observational Study
237(3)
Regression Coefficients
240(3)
The Multiple Linear Regression Model
240(1)
Interpretation of Regression Coefficients
241(2)
Specially Constructed Explanatory Variables
243(7)
A Squared Term for Curvature
244(1)
An Indicator Variable to Distinguish Between Two Groups
245(1)
Sets of Indicator Variables for Categorical Explanatory Variables with More Than Two Categories
246(1)
A Product Term for Interaction
247(2)
A Shorthand Notation for Model Description
249(1)
A Strategy for Data Analysis
250(1)
Graphical Methods for Data Exploration and Presentation
251(5)
A Matrix of Pairwise Scatterplots
251(3)
Coded Scatterplots
254(1)
Jittered Scatterplots
255(1)
Trellis Graphs
255(1)
Related Issues
256(1)
Computer Output
256(1)
Factorial Treatment Arrangement
257(1)
Summary
257(1)
Exercises
258(9)
Conceptual Exercises
258(2)
Computational Exercises
260(4)
Data Problems
264(1)
Answers to Conceptual Exercises
265(2)
Inferential Tools for Multiple Regression
267(37)
Case Studies
268(3)
Galileo's Data on the Motion of Falling Bodies--A Controlled Experiment
268(1)
The Energy Costs of Echolocation by Bats--An Observational Study
269(2)
Inferences About Regression Coefficients
271(9)
Least Squares Estimates and Standard Errors
271(2)
Tests and Confidence Intervals for Single Coefficients
273(2)
Tests and Confidence Intervals for Linear Combinations of Coefficients
275(4)
Prediction
279(1)
Extra-Sums-of-Squares F-Tests
280(5)
Comparing Sizes of Residuals in Hierarchical Models
280(1)
F-Test for the Joint Significance of Several Terms
281(1)
The Analysis of Variance Table
282(3)
Related Issues
285(6)
Further Notes on the R-Squared Statistic
285(2)
Improving Galileo's Design with Replication
287(1)
Variance Formulas for Linear Combinations of Regression Coefficients
288(2)
Further Notes About Polynomial Regression
290(1)
Finding Where the Mean Response is at its Maximum in Quadratic Regression
290(1)
The Principle of Occam's Razor
290(1)
Informal Tests in Model Fitting
291(1)
Summary
291(1)
Exercises
292(12)
Conceptual Exercises
292(2)
Computational Exercises
294(5)
Data Problems
299(3)
Answers to Conceptual Exercises
302(2)
Model Checking and Refinement
304(34)
Case Studies
305(5)
Alcohol Metabolism in Men and Women--An Observational Study
305(2)
The Blood-Brain Barrier--A Controlled Experiment
307(3)
Residual Plots
310(3)
A Strategy for Dealing with Influential Observations
313(3)
Case-Influence Statistics
316(5)
Leverages for Flagging Cases with Unusual Explanatory Variable Values
316(2)
Studentized Residuals for Flagging Outliers
318(1)
Cook's Distances for Flagging Influential Cases
319(1)
A Strategy for Using Case Influence Statistics
320(1)
Refining the Model
321(7)
Testing Terms
321(2)
Partial Residual Plots
323(5)
Related Issues
328(2)
Weighted Regression for Certain Types of Nonconstant Variance
328(1)
The Delta Method
328(1)
Measurement Errors in Explanatory Variables
329(1)
Summary
330(1)
Exercises
331(7)
Conceptual Exercises
331(1)
Computational Exercises
332(3)
Data Problems
335(2)
Answers to Conceptual Exercises
337(1)
Strategies for Variable Selection
338(36)
Case Studies
339(6)
State Average SAT Scores-An Observational Study
339(3)
Sex Discrimination in Employment-An Observational Study
342(3)
Specific Issues Relating to Many Explanatory Variables
345(5)
Objectives
345(2)
Loss of Precision
347(1)
A Strategy for Dealing with Many Explanatory Variables
347(3)
Sequential Variable Selection Techniques
350(4)
Forward Selection
350(1)
Backward Elimination
350(1)
Stepwise Regression
351(1)
Sequential Variable Selection with the SAT Data
351(2)
Compounded Uncertainty in Stepwise Procedures
353(1)
Model Selection Among All Subsets
354(4)
The Cp Statistic and Cp Plot
355(1)
Akaike's and the Bayes Information Criteria
356(2)
Posterior Beliefs About Different Models
358(1)
Analysis of the Sex Discrimination Data
358(4)
Related Issues
362(4)
The Trouble with Interpreting Significance When Explanatory Variables Are Correlated
362(2)
Regression for Adjustment and Ranking
364(1)
Saturated Second-Order Models
365(1)
Cross Validation
366(1)
Summary
366(1)
Exercises
367(7)
Conceptual Exercises
367(1)
Computational Exercises
368(2)
Data Problems
370(2)
Answers to Conceptual Exercises
372(2)
The Analysis of Variance for Two-Way Classifications
374(35)
Case Studies
375(3)
Intertidal Seaweed Grazers--A Randomized Experiment
375(2)
The Pygmalion Effect--A Randomized Experiment
377(1)
Additive and Nonadditive Models for Two-Way Tables
378(4)
The Additive Model
378(1)
The Saturated, Nonadditive Model
378(4)
A Strategy for Analyzing Two-Way Tables with Several Observations per Cell
382(1)
The Analysis of Variance F-Test for Additivity
382(1)
Analysis of the Seaweed Grazer Data
382(10)
Initial Assessment of Additivity, Outliers, and the Need for Transformation
382(2)
The Analysis of Variance Table from the Fit to the Saturated Model
384(2)
The Analysis of Variance Table for the Fit to the Additive Model
386(2)
Answers to Specific Questions of Interest Using Linear Combinations
388(2)
Answers to Specific Questions of Interest Using Multiple Regression with Indicator Variables
390(2)
Analysis of the Pygmalion Data
392(6)
Initial Exploration and Check on Additive Model
392(3)
Answering the Question of Interest with Regression
395(1)
A Closer Look at the Regression Estimate of Treatment Effect
396(1)
The p-Value in the Randomization Distribution
397(1)
Related Issues
398(5)
Additivity and Nonadditivities
398(2)
Orthogonal Contrasts
400(1)
Randomized Blocks and Paired-t Analyses
401(1)
Should Insignificant Block Effects be Eliminated from the Model?
401(1)
Multiple Comparisons
401(1)
An Alternate Parameterization for the Additive Model
401(2)
Summary
403(1)
Exercises
404(5)
Conceptual Exercises
404(1)
Computational Exercises
404(3)
Data Problems
407(1)
Answers to Conceptual Exercises
408(1)
Multifactor Studies Without Replication
409(27)
Case Studies
410(4)
Chimpanzees Learning Sign Language--A Controlled Experiment
410(1)
Effects of Ozone in Conjunction with Sulfur Dioxide and Water Stress on Soybean Yield--A Randomized Experiment
411(3)
Strategies for Analyzing Tables with One Observation per Cell
414(1)
Rationale for Designs with One Observation per Cell
414(1)
Strategy for Data Analysis in the Absence of Replicates
415(1)
Analysis of the Chimpanzee Learning Times Study
415(6)
Analysis of the Soybean Data
421(6)
Exploratory Analysis
422(4)
Answering Questions of Interest with the Fitted Models
426(1)
Related Issues
427(4)
Random Effects Models
427(1)
Nested Classifications in the Analysis of Variance
428(1)
Further Rationale for One Observation per Cell
429(2)
Uniformity Trials
431(1)
Summary
431(1)
Exercises
432(4)
Conceptual Exercises
432(1)
Computational Exercises
432(1)
Data Problems
433(2)
Answers to Conceptual Exercises
435(1)
Adjustment for Serial Correlation
436(26)
Case Studies
437(2)
Logging Practices and Water Quality--An Observational Study
437(1)
Measuring Global Warming--An Observational Study
438(1)
Comparing the Means of Two Time Series
439(6)
Serial Correlation and Its Effect on the Average of a Time Series
440(2)
The Standard Error of an Average in a Serially Correlated Time Series
442(1)
Estimating the First Serial Correlation Coefficient
442(2)
Pooling Estimates and Comparing Means of Two Independent Time Series with the Same First Serial Correlation
444(1)
Regression After Transformation in the AR(1) Model
445(3)
The Serial Correlation Coefficient Based on Regression Residuals
445(1)
Regression with Filtered Variables
445(3)
Determining if Serial Correlation is Present
448(2)
An Easy, Large-Sample Test for Serial Correlation
448(1)
The Nonparametric Runs Test
448(2)
Diagnostic Procedures for Judging the Adequacy of the AR(1) Model
450(4)
When Is a Transformation of a Time Series Indicated?
450(1)
The Partial Autocorrelation Function (PACF)
450(3)
Bayesian Information Criterion
453(1)
Related Issues
454(1)
Time Series Analysis is a Large-Sample Game
454(1)
The Autocorrelation Function
454(1)
Time Series Without a Time Series Package
454(1)
Negative Serial Correlation
454(1)
Summary
455(1)
Exercises
456(6)
Conceptual Exercises
456(1)
Computational Exercises
456(2)
Data Problems
458(3)
Answers to Conceptual Exercises
461(1)
Repeated Measures and Other Multivariate Responses
462(35)
Case Studies
463(3)
Sites of Short- and Long-Term Memory--A Controlled Experiment
463(1)
Oat Bran and Cholesterol--A Randomized Crossover Experiment
464(2)
Tools and Strategies for Analyzing Repeated Measures
466(5)
Types of Repeated Measures Studies
467(1)
Profile Plots for Graphical Exploration
468(1)
Strategies for Analyzing Repeated Measures
468(3)
Comparing the Means of Bivariate Responses in Two Groups
471(8)
Summary Statistics for Bivariate Responses
471(2)
Pooled Variability Estimates
473(1)
Hotelling's T2 Statistic
474(1)
Checking on Assumptions
475(1)
Confidence Ellipses and Individual Confidence Intervals for Differences in Bivariate Means
476(3)
One-Sample Analysis with Bivariate Responses
479(4)
Treatment Differences in the Oat Bran Study
479(1)
Summary Statistics for a Single Sample of Bivariate Responses
480(1)
Hotelling's T2 Test that the Means of a Bivariate Response are Both Zero
480(1)
Checking on Assumptions
481(2)
Related Issues
483(3)
Two-Sample Analysis with More Than Two Responses
483(1)
One-Sample Analysis with More Than Two Responses
484(1)
Multivariate Regression and Multivariate Analysis of Variance (MANOVA)
484(1)
Planned and Unplanned Summaries of Multivariate Measurements as Response Variables
484(1)
Planning an Experiment: Benefits of Repeated Measurements
485(1)
Related Issues
486(2)
Notes Concerning Correlation
486(2)
Star Plots for Multivariate Responses
488(1)
Summary
488(1)
Exercises
489(8)
Conceptual Exercises
489(1)
Computational Exercises
490(4)
Data Problems
494(2)
Answers to Conceptual Exercises
496(1)
Exploratory Tools for Summarizing Multivariate Responses
497(32)
Case Studies
498(4)
Magnetic Force on Rods in Printers--A Controlled Experiment
498(2)
Love and Marriage--An Observational Study
500(2)
Linear Combinations of Variables
502(2)
Principal Components Analysis
504(7)
The PCA Train
504(1)
Principal Components
504(3)
Variables Suggested by PCA
507(2)
Scatterplots in Principal Component Space
509(1)
The Factor Analysis Model and Principal Components Analysis
510(1)
PCA Usage
510(1)
Canonical Correlations Analysis
511(4)
Canonical Variables
511(2)
Variables Suggested by CCA
513(1)
Love and Marriage Example
513(2)
Introduction to Other Multivariate Tools
515(6)
Discriminant Function Analysis (DFA)
515(1)
Cluster Analysis
516(1)
Multidimensional Scaling
517(1)
Correspondence Analysis
518(1)
PCA and Empirical Orthogonal Functions (EOFs)
519(2)
Summary
521(1)
Exercises
522(7)
Conceptual Exercises
522(2)
Computational Exercises
524(1)
Data Problems
525(3)
Answers to Conceptual Exercises
528(1)
Comparisons of Proportions or Odds
529(23)
Case Studies
530(3)
Obesity and Heart Disease--An Observational Study
530(1)
Vitamin C and the Common Cold--A Randomized Experiment
531(1)
Smoking and Lung Cancer--A Retrospective Observational Study
532(1)
Inferences for the Difference of Two Proportions
533(5)
The Sampling Distribution of a Sample Proportion
533(2)
Sampling Distribution for the Difference between Two Sample Proportions
535(1)
Inferences about the Difference between Two Population Proportions
536(2)
Inference About the Ratio of Two Odds
538(4)
A Problem with the Difference between Proportions
538(1)
Odds
539(1)
The Ratio of Two Odds
539(2)
Sampling Distribution of the Log of the Estimated Odds Ratio
541(1)
Inference from Retrospective Studies
542(3)
Retrospective Studies
542(2)
Why the Odds Ratio Is the Only Appropriate Parameter if the Sampling Is Retrospective
544(1)
Summary
545(1)
Exercises
546(6)
Conceptual Exercises
546(2)
Computational Exercises
548(2)
Data Problems
550(1)
Answers to Conceptual Exercises
550(2)
More Tools for Tables of Counts
552(27)
Case Studies
553(2)
Sex Role Stereotypes and Personnel Decisions--A Randomized Experiment
553(1)
Death Penalty and Race of Murder Victim--An Observational Study
554(1)
Population Models for 2 x 2 Tables of Counts
555(4)
Hypotheses of Homogeneity and of Independence
555(1)
Sampling Schemes Leading to 2 x 2 Tables
556(2)
Testable Hypotheses and Estimable Parameters
558(1)
The Chi-Squared Test
559(3)
The Pearson Chi-Squared Test for Goodness of Fit
559(1)
Chi-Squared Test of Independence in a 2 x 2 Table
559(1)
Equivalence of Several Tests for 2 x 2 Tables
560(2)
Fisher's Exact Test: The Randomization (Permutation) Test for 2 x 2 Tables
562(4)
The Randomization Distribution of the Difference in Sample Proportions
562(2)
The Hypergeometric Formula for One-Sided p-Values
564(1)
Fisher's Exact Test for Observational Studies
564(1)
Fisher's Exact Test Versus Other Tests
564(2)
Combining Results from Several Tables with Equal Odds Ratios
566(4)
The Mantel-Haenszel Excess
566(1)
The Mantel-Haenszel Test for Equal Odds in Several 2 x 2 Tables
567(2)
Estimate of the Common Odds Ratio
569(1)
Related Issues
570(1)
r x c Tables of Counts
570(1)
Higher-Dimensional Tables of Counts
570(1)
Summary
571(1)
Exercises
572(7)
Conceptual Exercises
572(1)
Computational Exercises
573(3)
Data Problems
576(1)
Answers to Conceptual Exercises
577(2)
Logistic Regression for Binary Response Variables
579(30)
Case Studies
580(3)
Survival in the Donner Party--An Observational Study
580(1)
Birdkeeping and Lung Cancer--A Retrospective Observational Study
580(3)
The Logistic Regression Model
583(4)
Logistic Regression as a Generalized Linear Model
584(1)
Interpretation of Coefficients
585(2)
Estimation of Logistic Regression Coefficients
587(5)
Maximum Likelihood Parameter Estimation
587(3)
Tests and Confidence Intervals for Single Coefficients
590(2)
The Drop-In-Deviance Test
592(3)
Strategies for Data Analysis Using Logistic Regression
595(1)
Exploratory Analysis
595(1)
Analyses of Case Studies
596(4)
Analysis of Donner Party Data
596(1)
Analysis of Birdkeeping and Lung Cancer Data
597(3)
Related Issues
600(1)
Matched Case-Control Studies
600(1)
Probit Regression
600(1)
Discriminant Analysis Using Logistic Regression
601(1)
Summary
601(1)
Exercises
602(7)
Conceptual Exercises
602(1)
Computational Exercises
603(2)
Data Problems
605(3)
Answers to Conceptual Exercises
608(1)
Logistic Regression for Binomial Counts
609(35)
Case Studies
610(4)
Island Size and Bird Extinctions--An Observational Study
610(2)
Moth Coloration and Natural Selection--A Randomized Experiment
612(2)
Logistic Regression for Binomial Responses
614(1)
Binomial Responses
614(1)
The Logistic Regression Model for Binomial Responses
614(1)
Model Assessment
615(3)
Scatterplot of Empirical Logits Versus an Explanatory Variable
615(1)
Examination of Residuals
615(1)
The Deviance Goodness-of-Fit Test
616(2)
Inferences about Logistic Regression Coefficients
618(3)
Wald's Tests and Confidence Intervals for Single Coefficients
618(2)
The Drop-In-Deviance Test
620(1)
Extra-Binomial Variation
621(3)
Extra-Binomial Variation and the Logistic Regression Model
621(1)
Checking for Extra-Binomial Variation
621(1)
Quasi-Likelihood Inference When Extra-Binomial Variation Is Present
622(2)
Analysis of Moth Predation Data
624(3)
Related Issues
627(9)
Why the Deviance Changes When Binary Observations Are Grouped
627(1)
Logistic Models for Multilevel Categorical Responses
628(2)
The Maximum Likelihood Principle
630(3)
Bayesian Inference
633(3)
Summary
636(1)
Exercises
637(7)
Conceptual Exercises
637(1)
Computational Exercises
638(2)
Data Problems
640(2)
Answers to Conceptual Exercises
642(2)
Log-Linear Regression for Poisson Counts
644(25)
Case Studies
645(3)
Age and Mating Success of Male Elephants--An Observational Study
645(1)
Characteristics Associated with Salamander Habitat
646(2)
Log-Linear Regression for Poisson Responses
648(3)
Poisson Responses
648(1)
The Poisson Log-Linear Model
649(1)
Estimation by Maximum Likelihood
650(1)
Model Assessment
651(3)
Scatterplot of Logged Counts Versus an Explanatory Variable
651(1)
Residuals
651(1)
The Deviance Goodness-of-Fit Test
652(2)
The Pearson Chi-Squared Goodness-of Fit Test
654(1)
Inferences about Log-Linear Regression Coefficients
654(2)
Wald's Test and Confidence Interval for Single Coefficients
654(1)
The Drop-In-Deviance Test
655(1)
Extra-Poisson Variation and the Log-Linear Model
656(4)
Extra-Poisson Variation
656(1)
Checking for Extra-Poisson Variation
656(1)
Inferences When Extra-Poisson Variation Is Present
657(3)
Related Issues
660(2)
Log-Linear Models for Testing Independence in Tables of Counts
660(1)
Poisson Counts from Varying Effort
661(1)
Summary
662(1)
Exercises
663(6)
Conceptual Exercises
663(1)
Computational Exercises
664(1)
Data Problems
665(3)
Answers to Conceptual Exercises
668(1)
Elements of Research Design
669(24)
Case Study
670(1)
Biological Control of a Noxious Weed--A Randomized Experiment
670(1)
Considerations in Forming Research Objectives
670(1)
Research Design Tool Kit
671(3)
Controls and Placebos
671(1)
Blinding
672(1)
Blocking
672(1)
Stratification
672(1)
Covariates
672(1)
Randomization
673(1)
Random Sampling
673(1)
Replication
673(1)
Balance
674(1)
Design Choices that Affect Accuracy and Precision
674(4)
Attaching Desired Precision to Practical Significance
674(1)
How to Improve a Confidence Interval
675(3)
Choosing a Sample Size
678(2)
Studies with a Numerical Response
678(1)
Studies Comparing Two Proportions
679(1)
Sample Size for Estimating a Regression Coefficient
679(1)
Steps in Designing a Study
680(7)
Stating the Objective
680(1)
Determining the Scope of Inference
681(1)
Understanding the System
682(1)
Deciding How to Measure a Response
682(1)
Listing Factors that Can Affect the Response
683(1)
Planning the Conduct of the Experiment
684(1)
Outlining the Statistical Analysis
684(1)
Determining the Sample Size
685(2)
Related Issue--A Factor of Four
687(1)
Summary
688(1)
Exercises
689(4)
Conceptual Exercises
689(1)
Study Designs for Discussion
689(1)
Computational Exercises
690(1)
Design Problems
691(1)
Answers to Conceptual Exercises
692(1)
Factorial Treatment Arrangements and Blocking Designs
693(22)
Case Study
694(1)
Amphibian Crisis Linked to Ultraviolet Light-A Randomized Experiment
694(1)
Treatments
695(2)
Choosing Treatment Levels
695(1)
The Rationale for Several Factors
696(1)
Factorial Arrangement of Treatment Levels
697(9)
Definition and Terminology for a Factorial Arrangement
697(1)
The 22 Factorial Structure
697(3)
The 23 Factorial Structure
700(1)
The 32 Factorial Structure
701(3)
Higher Order Factorial Arrangements
704(2)
Blocking
706(4)
Randomized Blocks
706(2)
Latin Square Blocking
708(1)
Split Plot Designs
709(1)
Summary
710(1)
Exercises
711(4)
Conceptual Exercises
711(2)
Computational Exercises
713(1)
Data Problems
713(1)
Answers to Conceptual Exercises
713(2)
APPENDIX A Tables 715(17)
A.1 Probabilities of the Standard Normal Distribution
716(2)
A.2 Selected Percentiles of t-Distributions
718(1)
A.3 Selected Percentiles of Chi-Squared Distributions
719(1)
A.4 Selected Percentiles of F-Distributions
720(8)
A.5 Selected Percentiles of Studentized Range Distributions
728(4)
APPENDIX B Bibliography 732(2)
Index 734


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