Applied regression analysis and other multivariable methods:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Belmont, CA
Duxbury
2008
|
| Edition: | 4. ed. |
| Series: | Duxbury applied series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXI, 906 S. graph. Darst. 24 cm |
| ISBN: | 0495384968 9780495384960 |
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MARC
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| 245 | 1 | 0 | |a Applied regression analysis and other multivariable methods |c David G. Kleinbaum ... |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a Belmont, CA |b Duxbury |c 2008 | |
| 300 | |a XXI, 906 S. |b graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Duxbury applied series | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Multivariate analysis | |
| 650 | 4 | |a Regression analysis | |
| 650 | 0 | 7 | |a Regressionsanalyse |0 (DE-588)4129903-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Multivariate Analyse |0 (DE-588)4040708-1 |2 gnd |9 rswk-swf |
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| 883 | 1 | |8 3\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Record in the Search Index
| _version_ | 1804137991823687680 |
|---|---|
| adam_text | Contents
CONCEPTS
AND EXAMPLES OF RESEARCH
1.1
Concepts
1
1.2
Examples
2
1.3
Concluding Remarks
References
5
CLASSIFICATION OF VARIABLES
AND THE CHOICE OF ANALYSIS
7
2.1
Classification of Variables
7
2.2
Overlapping of Classification Schemes
11
2.3
Choice of Analysis
11
References
13
BASIC STATISTICS
:
A REVIEW
14
3.1
Preview
14
3.2
Descriptive Statistics
15
3.3
Random Variables and Distributions
17
3.4
Sampling Distributions of t,
χ2,
and
F
21
3.5
Statistical Inference: Estimation
23
3.6
Statistical Inference: Hypothesis Testing
26
XIII
xiv Contents
3.7
Error
Rates, Power, and Sample
Size
30
Problems 32
References
35
INTRODUCTION TO REGRESSION ANALYSIS
36
4.1
Preview
36
4.2
Association versus Causality
37
4.3
Statistical versus Deterministic Models
39
4.4
Concluding Remarks
40
References
40
STRAIGHT-LINE REGRESSION ANALYSIS
41
5.1
Preview
41
5.2
Regression with a Single Independent Variable
41
5.3
Mathematical Properties of a Straight Line
44
5.4
Statistical Assumptions for a Straight-line Model
45
5.5
Determining the Best-fitting Straight Line
49
5.6
Measure of the Quality of the Straight-line Fit and Estimate of
σ2
53
5.7
Inferences About the Slope and Intercept
54
5.8
Interpretations of Tests for Slope and Intercept
56
5.9
Inferences About the Regression Line
μγ χ = β0 + βχΧ
59
5.10
Prediction of a New Value of Fat Xo
61
5.11
Assessing the Appropriateness of the Straight-line Model
62
Problems
62
References
90
THE CORRELATION COEFFICIENT AND
STRAIGHT-LINE REGRESSION ANALYSIS
91
6.1
Definition of
r
91
6.2
r
as a Measure of Association
92
6.3
The Bivariate Normal Distribution
95
6.4
r
and the Strength of the Straight-line Relationship
96
6.5
What
г
Does Not Measure
98
6.6
Tests of Hypotheses and Confidence Intervals for the
Correlation Coefficient
99
6.7
Testing for the Equality of Two Correlations
102
Problems
104
References
106
Contents xv
THE ANALYSIS-OF-VARIANCE TABLE
107
7.1
Preview
107
7.2
The ANOVA Table for Straight-line Regression
107
Problems
111
MULTIPLE REGRESSION ANALYSIS:
GENERAL CONSIDERATIONS
114
8.1
Preview
114
8.2
Multiple Regression Models
115
8.3
Graphical Look at the Problem
116
8.4
Assumptions of Multiple Regression
118
8.5
Determining the Best Estimate of the Multiple Regression Equation
121
8.6
The ANOVA Table for Multiple Regression
122
8.7
Numerical Examples
124
Problems
126
References
138
TESTING HYPOTHESES IN MULTIPLE REGRESSION
139
9.1
Preview
139
9.2
Test for Significant Overall Regression
140
9.3
Partial
F
Test
141
9.4
Multiple Partial
F
Test
146
9.5
Strategies for Using Partial
F
Tests
148
9.6
Tests Involving the Intercept
153
Problems
154
References
162
CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL
163
10.1
Preview
163
10.2
Correlation Matrix
164
10.3
Multiple Correlation Coefficient
165
10.4
Relationship of
Ry¡Xí¡
Xl
,..
Xi to the Multivariate Normal Distribution
167
10.5
Partial Correlation Coefficient
168
10.6
Alternative Representation of the Regression Model
175
10.7
Multiple Partial Correlation
175
10.8
Concluding Remarks
177
Problems
177
References
188
xvi Contents
CONFOUNDING AND INTERACTION IN REGRESSION
189
11.1
Preview
189
11.2
Overview
189
11.3
Interaction in Regression
191
11.4
Confounding in Regression
198
11.5
Summary and Conclusions
203
Problems
204
References
216
DUMMY VARIABLES IN REGRESSION
217
12.1
Preview
217
12.2
Definitions
217
12.3
Rule for Defining Dummy Variables
218
12.4
Comparing Two Straight-line Regression Equations: An Example
219
12.5
Questions for Comparing Two Straight Lines
220
12.6
Methods of Comparing Two Straight Lines
221
12.7
Method I: Using Separate Regression Fits to Compare
Two Straight Lines
222
12.8
Method
П:
Using a Single Regression Equation to Compare
Two Straight Lines
227
12.9
Comparison of Methods I and II
230
12.10
Testing Strategies and Interpretation: Comparing Two Straight Lines
230
12.11
Other Dummy Variable Models
232
12.12
Comparing Four Regression Equations
234
12.13
Comparing Several Regression Equations Involving
Two Nominal Variables
236
Problems
241
References
263
ANALYSIS OF COVARIANCE AND OTHER
METHODS FOR ADJUSTING CONTINUOUS DATA
264
13.1
Preview
264
13.2
Adjustment Problem
264
13.3
Analysis of Covariance
266
13.4
Assumption of Parallelism: A Potential Drawback
268
13.5
Analysis of Covariance: Several Groups and Several Covariates
269
13.6
Comments and Cautions
271
13.7
Summary
274
Problems
274
Reference
286
Contents xvii
REGRESSION
DIAGNOSTICS
287
14.1 Preview 287
14.2 Simple
Approaches to Diagnosing
Problems in Data 288
14.3 Residual
Analysis: Detecting Outliers and Violations
of Model Assumptions
295
14.4
Alternate Strategies of Analysis
303
14.5
Collinearity
305
14.6
Scaling Problems
319
14.7
Diagnostics Example
319
14.8
An Important Caution
328
Problems
329
References
347
POLYNOMIAL REGRESSION
349
15.1
Preview
349
15.2
Polynomial Models
350
15.3
Least-squares Procedure for Fitting a Parabola
350
15.4
ANOVA Table for Second-order Polynomial Regression
352
15.5
Inferences Associated with Second-order
Polynomial Regression
352
15.6
Example Requiring a Second-order Model
354
15.7
Fitting and Testing Higher-order Models
357
15.8
Lack-of-fit Tests
358
15.9
Orthogonal Polynomials
360
15.10
Strategies for Choosing a Polynomial Model
369
Problems
370
SELECTING THE BEST REGRESSION EQUATION
383
16.1
Preview
383
16.2
Steps in Selecting the Best Regression Equation
384
16.3
Step
1 :
Specifying the Maximum Model
384
16.4
Step
2:
Specifying a Criterion for Selecting a Model
387
16.5
Step
3:
Specifying a Strategy for Selecting Variables
389
16.6
Step
4:
Conducting the Analysis
398
16.7
Step
5:
Evaluating Reliability with Split Samples
398
16.8
Example Analysis of Actual Data
400
16.9
Issues in Selecting the Most Valid Model
406
Problems
406
References
419
xviii Contents
ONE-WAY ANALYSIS OF VARIANCE
420
17.1
Preview
420
17.2
One-way ANOVA: The Problem, Assumptions, and Data Configuration
423
17.3
Methodology for One-way Fixed-effects ANOVA
426
17.4
Regression Model for Fixed-effects One-way ANOVA
432
17.5
Fixed-effects Model for One-way ANOVA
435
17.6
Random-effects Model for One-way ANOVA
438
17.7
Multiple-comparison Procedures for Fixed-effects One-way ANOVA
441
17.8
Choosing a Multiple-comparison Technique
452
17.9
Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares
453
Problems
459
References
480
RANDOMIZED BLOCKS
:
SPECIAL CASE OF TWO-WAY ANOVA
481
18.1
Preview
481
18.2
Equivalent Analysis of a Matched-pairs Experiment
485
18.3
Principle of Blocking
488
18.4
Analysis of a Randomized-blocks Experiment
490
18.5
ANOVA Table for a Randomized-blocks Experiment
492
18.6
Regression Models for a Randomized-blocks Experiment
496
18.7
Fixed-effects ANOVA Model for a Randomized-blocks Experiment
499
Problems
500
References
512
TWO-WAY ANOVA WITH EQUAL CELL NUMBERS
513
19.1
Preview
513
19.2
Using a Table of Cell Means
515
19.3
General Methodology
519
19.4
F
Tests for Two-way ANOVA
524
19.5
Regression Model for Fixed-effects Two-way ANOVA
527
19.6
Interactions in Two-way ANOVA
531
19.7
Random- and Mixed-effects Two-way ANOVA Models
538
Problems
541
References
557
TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS
558
20.1
Preview
558
20.2
Problem with Unequal Cell Numbers: Nonorthogonality
560
20.3
Regression Approach for Unequal Cell Sample Sizes
564
Contents xix
20.4 Higher-
way
ANOVA 568
Problems 569
References
585
THE METHOD OF
MAXIMUM
LIKELIHOOD
586
21.1 Preview 586
21.2
The Principle of
Maximum
Likelihood
586
21.3
Statistical Inference Using
Maximum
Likelihood
589
21.4
Summary
601
Problems
601
References
603
LOGISTIC REGRESSION ANALYSIS
604
22.1
Preview
604
22.2
The Logistic Model
604
22.3
Estimating the Odds Ratio Using Logistic Regression
606
22.4
A Numerical Example of Logistic Regression
612
22.5
Theoretical Considerations
619
22.6
An Example of Conditional ML Estimation Involving
Pair-matched Data with Unmatched Covariates
625
22.7
Summary
629
Problems
630
References
634
POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION
635
23.1
Preview
635
23.2
Why Not Use Binary Regression?
636
23.3
An Example of Polytomous Logistic Regression: One Predictor,
Three Outcome Categories
636
23.4
An Example: Extending the Polytomous Logistic Model
to Several Predictors
641
23.5
Ordinal Logistic Regression: Overview
645
23.6
A Simple Hypothetical Example: Three Ordinal Categories
and One Dichotomous Exposure Variable
646
23.7
Ordinal Logistic Regression Example Using Real Data
with Four Ordinal Categories and Three Predictor Variables
650
23.8
Summary
655
Problems
656
References
660
xx Contents
POISSON
REGRESSION
ANALYSIS
661
24.1 Preview 661
24.2 The
Poisson
Distribution 661
24.3
An
Example of
Poisson
Regression
663
24.4
Poisson
Regression: General Considerations
666
24.5
Measures of Goodness of Fit
669
24.6
Continuation of Skin Cancer Data Example
671
24.7
A Second Illustration of
Poisson
Regression Analysis
676
24.8
Summary
679
Problems
679
References
692
ANALYSIS OF CORRELATED DATA PART
1:
THE GENERAL
LINEAR MIXED MODEL
693
25.1
Preview
693
25.2
Examples
696
25.3
General Linear Mixed Model Approach
703
25.4
Example: Study of Effects of an Air Pollution Episode on FEV
1
Levels
716
25.5
Summary
—
Analysis of Correlated Data: Part
1 727
Problems
728
References
734
ANALYSIS OF CORRELATED DATA PART
2:
RANDOM EFFECTS
AND OTHER ISSUES
735
26.1
Preview
735
26.2
Random Effects Revisited
735
26.3
Results for Models with Random Effects Applied to Air Pollution Study Data
739
26.4
Second Example
—
Analysis of Posture Measurement Data
748
26.5
Recommendations about Choice of Correlation Structure
767
26.6
Analysis of Data for Discrete Outcomes
769
Problems
770
References
787
SAMPLE SIZE PLANNING FOR LINEAR AND LOGISTIC
REGRESSION AND ANALYSIS OF VARIANCE
788
27.1
Preview
788
27.2
Review: Sample Size Calculations for Comparisons of Means and Proportions
789
27.3
Sample Size Planning for Linear Regression
791
27.4
Sample Size Planning for Logistic Regression
794
Contents xxi
27.5 Power and Sample
Size
Determination
for
Linear Models:
A
General
Approach
797
27.6 Sample
Size
Determination
for Matched Case-control
Studies with a Dichotomous Outcome
811
27.7
Practical Considerations and Cautions
814
Problems
815
References
816
APPENDIX A—TABLES
819
A.
1
Standard Normal Cumulative Probabilities
820
A.2 Percentiles of the
t
Distribution
823
A.3 Percentiles of the Chi-square Distribution
824
A.4 Percentiles of the
F
Distribution
825
A.5 Values of
{
In —^
832
ι
—
ľ
A.6 Upper a Point of Studentized Range
834
A.7 Orthogonal Polynomial Coefficients
836
A.
8
A Bonferroni Corrected Jackknife Residual Critical Values
837
A.8B Bonferroni Corrected Studentized Residual Critical Values
837
A.9 Critical Values for Leverages
838
A.
10
Critical Values for the Maximum of
n
Values of Cook s (n
—
k
—
)d¡
840
APPENDIX
В—
MATRICES AND THEIR
RELATIONSHIP TO REGRESSION ANALYSIS
841
APPENDIX C—ANSWERS TO SELECTED PROBLEMS
853
INDEX
893
|
| adam_txt |
Contents
CONCEPTS
AND EXAMPLES OF RESEARCH
1.1
Concepts
1
1.2
Examples
2
1.3
Concluding Remarks
References
5
CLASSIFICATION OF VARIABLES
AND THE CHOICE OF ANALYSIS
7
2.1
Classification of Variables
7
2.2
Overlapping of Classification Schemes
11
2.3
Choice of Analysis
11
References
13
BASIC STATISTICS
:
A REVIEW
14
3.1
Preview
14
3.2
Descriptive Statistics
15
3.3
Random Variables and Distributions
17
3.4
Sampling Distributions of t,
χ2,
and
F
21
3.5
Statistical Inference: Estimation
23
3.6
Statistical Inference: Hypothesis Testing
26
XIII
xiv Contents
3.7
Error
Rates, Power, and Sample
Size
30
Problems 32
References
35
INTRODUCTION TO REGRESSION ANALYSIS
36
4.1
Preview
36
4.2
Association versus Causality
37
4.3
Statistical versus Deterministic Models
39
4.4
Concluding Remarks
40
References
40
STRAIGHT-LINE REGRESSION ANALYSIS
41
5.1
Preview
41
5.2
Regression with a Single Independent Variable
41
5.3
Mathematical Properties of a Straight Line
44
5.4
Statistical Assumptions for a Straight-line Model
45
5.5
Determining the Best-fitting Straight Line
49
5.6
Measure of the Quality of the Straight-line Fit and Estimate of
σ2
53
5.7
Inferences About the Slope and Intercept
54
5.8
Interpretations of Tests for Slope and Intercept
56
5.9
Inferences About the Regression Line
μγ\χ = β0 + βχΧ
59
5.10
Prediction of a New Value of Fat Xo
61
5.11
Assessing the Appropriateness of the Straight-line Model
62
Problems
62
References
90
THE CORRELATION COEFFICIENT AND
STRAIGHT-LINE REGRESSION ANALYSIS
91
6.1
Definition of
r
91
6.2
r
as a Measure of Association
92
6.3
The Bivariate Normal Distribution
95
6.4
r
and the Strength of the Straight-line Relationship
96
6.5
What
г
Does Not Measure
98
6.6
Tests of Hypotheses and Confidence Intervals for the
Correlation Coefficient
99
6.7
Testing for the Equality of Two Correlations
102
Problems
104
References
106
Contents xv
THE ANALYSIS-OF-VARIANCE TABLE
107
7.1
Preview
107
7.2
The ANOVA Table for Straight-line Regression
107
Problems
111
MULTIPLE REGRESSION ANALYSIS:
GENERAL CONSIDERATIONS
114
8.1
Preview
114
8.2
Multiple Regression Models
115
8.3
Graphical Look at the Problem
116
8.4
Assumptions of Multiple Regression
118
8.5
Determining the Best Estimate of the Multiple Regression Equation
121
8.6
The ANOVA Table for Multiple Regression
122
8.7
Numerical Examples
124
Problems
126
References
138
TESTING HYPOTHESES IN MULTIPLE REGRESSION
139
9.1
Preview
139
9.2
Test for Significant Overall Regression
140
9.3
Partial
F
Test
141
9.4
Multiple Partial
F
Test
146
9.5
Strategies for Using Partial
F
Tests
148
9.6
Tests Involving the Intercept
153
Problems
154
References
162
CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL
163
10.1
Preview
163
10.2
Correlation Matrix
164
10.3
Multiple Correlation Coefficient
165
10.4
Relationship of
Ry¡Xí¡
Xl
,.
Xi to the Multivariate Normal Distribution
167
10.5
Partial Correlation Coefficient
168
10.6
Alternative Representation of the Regression Model
175
10.7
Multiple Partial Correlation
175
10.8
Concluding Remarks
177
Problems
177
References
188
xvi Contents
CONFOUNDING AND INTERACTION IN REGRESSION
189
11.1
Preview
189
11.2
Overview
189
11.3
Interaction in Regression
191
11.4
Confounding in Regression
198
11.5
Summary and Conclusions
203
Problems
204
References
216
DUMMY VARIABLES IN REGRESSION
217
12.1
Preview
217
12.2
Definitions
217
12.3
Rule for Defining Dummy Variables
218
12.4
Comparing Two Straight-line Regression Equations: An Example
219
12.5
Questions for Comparing Two Straight Lines
220
12.6
Methods of Comparing Two Straight Lines
221
12.7
Method I: Using Separate Regression Fits to Compare
Two Straight Lines
222
12.8
Method
П:
Using a Single Regression Equation to Compare
Two Straight Lines
227
12.9
Comparison of Methods I and II
230
12.10
Testing Strategies and Interpretation: Comparing Two Straight Lines
230
12.11
Other Dummy Variable Models
232
12.12
Comparing Four Regression Equations
234
12.13
Comparing Several Regression Equations Involving
Two Nominal Variables
236
Problems
241
References
263
ANALYSIS OF COVARIANCE AND OTHER
METHODS FOR ADJUSTING CONTINUOUS DATA
264
13.1
Preview
264
13.2
Adjustment Problem
264
13.3
Analysis of Covariance
266
13.4
Assumption of Parallelism: A Potential Drawback
268
13.5
Analysis of Covariance: Several Groups and Several Covariates
269
13.6
Comments and Cautions
271
13.7
Summary
274
Problems
274
Reference
286
Contents xvii
REGRESSION
DIAGNOSTICS
287
14.1 Preview 287
14.2 Simple
Approaches to Diagnosing
Problems in Data 288
14.3 Residual
Analysis: Detecting Outliers and Violations
of Model Assumptions
295
14.4
Alternate Strategies of Analysis
303
14.5
Collinearity
305
14.6
Scaling Problems
319
14.7
Diagnostics Example
319
14.8
An Important Caution
328
Problems
329
References
347
POLYNOMIAL REGRESSION
349
15.1
Preview
349
15.2
Polynomial Models
350
15.3
Least-squares Procedure for Fitting a Parabola
350
15.4
ANOVA Table for Second-order Polynomial Regression
352
15.5
Inferences Associated with Second-order
Polynomial Regression
352
15.6
Example Requiring a Second-order Model
354
15.7
Fitting and Testing Higher-order Models
357
15.8
Lack-of-fit Tests
358
15.9
Orthogonal Polynomials
360
15.10
Strategies for Choosing a Polynomial Model
369
Problems
370
SELECTING THE BEST REGRESSION EQUATION
383
16.1
Preview
383
16.2
Steps in Selecting the Best Regression Equation
384
16.3
Step
1 :
Specifying the Maximum Model
384
16.4
Step
2:
Specifying a Criterion for Selecting a Model
387
16.5
Step
3:
Specifying a Strategy for Selecting Variables
389
16.6
Step
4:
Conducting the Analysis
398
16.7
Step
5:
Evaluating Reliability with Split Samples
398
16.8
Example Analysis of Actual Data
400
16.9
Issues in Selecting the Most Valid Model
406
Problems
406
References
419
xviii Contents
ONE-WAY ANALYSIS OF VARIANCE
420
17.1
Preview
420
17.2
One-way ANOVA: The Problem, Assumptions, and Data Configuration
423
17.3
Methodology for One-way Fixed-effects ANOVA
426
17.4
Regression Model for Fixed-effects One-way ANOVA
432
17.5
Fixed-effects Model for One-way ANOVA
435
17.6
Random-effects Model for One-way ANOVA
438
17.7
Multiple-comparison Procedures for Fixed-effects One-way ANOVA
441
17.8
Choosing a Multiple-comparison Technique
452
17.9
Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares
453
Problems
459
References
480
RANDOMIZED BLOCKS
:
SPECIAL CASE OF TWO-WAY ANOVA
481
18.1
Preview
481
18.2
Equivalent Analysis of a Matched-pairs Experiment
485
18.3
Principle of Blocking
488
18.4
Analysis of a Randomized-blocks Experiment
490
18.5
ANOVA Table for a Randomized-blocks Experiment
492
18.6
Regression Models for a Randomized-blocks Experiment
496
18.7
Fixed-effects ANOVA Model for a Randomized-blocks Experiment
499
Problems
500
References
512
TWO-WAY ANOVA WITH EQUAL CELL NUMBERS
513
19.1
Preview
513
19.2
Using a Table of Cell Means
515
19.3
General Methodology
519
19.4
F
Tests for Two-way ANOVA
524
19.5
Regression Model for Fixed-effects Two-way ANOVA
527
19.6
Interactions in Two-way ANOVA
531
19.7
Random- and Mixed-effects Two-way ANOVA Models
538
Problems
541
References
557
TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS
558
20.1
Preview
558
20.2
Problem with Unequal Cell Numbers: Nonorthogonality
560
20.3
Regression Approach for Unequal Cell Sample Sizes
564
Contents xix
20.4 Higher-
way
ANOVA 568
Problems 569
References
585
THE METHOD OF
MAXIMUM
LIKELIHOOD
586
21.1 Preview 586
21.2
The Principle of
Maximum
Likelihood
586
21.3
Statistical Inference Using
Maximum
Likelihood
589
21.4
Summary
601
Problems
601
References
603
LOGISTIC REGRESSION ANALYSIS
604
22.1
Preview
604
22.2
The Logistic Model
604
22.3
Estimating the Odds Ratio Using Logistic Regression
606
22.4
A Numerical Example of Logistic Regression
612
22.5
Theoretical Considerations
619
22.6
An Example of Conditional ML Estimation Involving
Pair-matched Data with Unmatched Covariates
625
22.7
Summary
629
Problems
630
References
634
POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION
635
23.1
Preview
635
23.2
Why Not Use Binary Regression?
636
23.3
An Example of Polytomous Logistic Regression: One Predictor,
Three Outcome Categories
636
23.4
An Example: Extending the Polytomous Logistic Model
to Several Predictors
641
23.5
Ordinal Logistic Regression: Overview
645
23.6
A "Simple" Hypothetical Example: Three Ordinal Categories
and One Dichotomous Exposure Variable
646
23.7
Ordinal Logistic Regression Example Using Real Data
with Four Ordinal Categories and Three Predictor Variables
650
23.8
Summary
655
Problems
656
References
660
xx Contents
POISSON
REGRESSION
ANALYSIS
661
24.1 Preview 661
24.2 The
Poisson
Distribution 661
24.3
An
Example of
Poisson
Regression
663
24.4
Poisson
Regression: General Considerations
666
24.5
Measures of Goodness of Fit
669
24.6
Continuation of Skin Cancer Data Example
671
24.7
A Second Illustration of
Poisson
Regression Analysis
676
24.8
Summary
679
Problems
679
References
692
ANALYSIS OF CORRELATED DATA PART
1:
THE GENERAL
LINEAR MIXED MODEL
693
25.1
Preview
693
25.2
Examples
696
25.3
General Linear Mixed Model Approach
703
25.4
Example: Study of Effects of an Air Pollution Episode on FEV
1
Levels
716
25.5
Summary
—
Analysis of Correlated Data: Part
1 727
Problems
728
References
734
ANALYSIS OF CORRELATED DATA PART
2:
RANDOM EFFECTS
AND OTHER ISSUES
735
26.1
Preview
735
26.2
Random Effects Revisited
735
26.3
Results for Models with Random Effects Applied to Air Pollution Study Data
739
26.4
Second Example
—
Analysis of Posture Measurement Data
748
26.5
Recommendations about Choice of Correlation Structure
767
26.6
Analysis of Data for Discrete Outcomes
769
Problems
770
References
787
SAMPLE SIZE PLANNING FOR LINEAR AND LOGISTIC
REGRESSION AND ANALYSIS OF VARIANCE
788
27.1
Preview
788
27.2
Review: Sample Size Calculations for Comparisons of Means and Proportions
789
27.3
Sample Size Planning for Linear Regression
791
27.4
Sample Size Planning for Logistic Regression
794
Contents xxi
27.5 Power and Sample
Size
Determination
for
Linear Models:
A
General
Approach
797
27.6 Sample
Size
Determination
for Matched Case-control
Studies with a Dichotomous Outcome
811
27.7
Practical Considerations and Cautions
814
Problems
815
References
816
APPENDIX A—TABLES
819
A.
1
Standard Normal Cumulative Probabilities
820
A.2 Percentiles of the
t
Distribution
823
A.3 Percentiles of the Chi-square Distribution
824
A.4 Percentiles of the
F
Distribution
825
A.5 Values of
{
In —^
832
ι
—
ľ
A.6 Upper a Point of Studentized Range
834
A.7 Orthogonal Polynomial Coefficients
836
A.
8
A Bonferroni Corrected Jackknife Residual Critical Values
837
A.8B Bonferroni Corrected Studentized Residual Critical Values
837
A.9 Critical Values for Leverages
838
A.
10
Critical Values for the Maximum of
n
Values of Cook's (n
—
k
—
\)d¡
840
APPENDIX
В—
MATRICES AND THEIR
RELATIONSHIP TO REGRESSION ANALYSIS
841
APPENDIX C—ANSWERS TO SELECTED PROBLEMS
853
INDEX
893 |
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| discipline_str_mv | Wirtschaftswissenschaften |
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| genre_facet | Einführung |
| id | DE-604.BV035051729 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:56:49Z |
| indexdate | 2024-07-09T21:21:06Z |
| institution | BVB |
| isbn | 0495384968 9780495384960 |
| language | English |
| lccn | 2006940618 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016720393 |
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| physical | XXI, 906 S. graph. Darst. 24 cm |
| publishDate | 2008 |
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| publisher | Duxbury |
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| series2 | Duxbury applied series |
| spelling | Applied regression analysis and other multivariable methods David G. Kleinbaum ... 4. ed. Belmont, CA Duxbury 2008 XXI, 906 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Duxbury applied series Includes bibliographical references and index Multivariate analysis Regression analysis Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content Multivariate Analyse (DE-588)4040708-1 s Methode (DE-588)4038971-6 s 2\p DE-604 Regressionsanalyse (DE-588)4129903-6 s 3\p DE-604 Kleinbaum, David G. 1941- Sonstige (DE-588)114206422 oth Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016720393&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Applied regression analysis and other multivariable methods Multivariate analysis Regression analysis Regressionsanalyse (DE-588)4129903-6 gnd Multivariate Analyse (DE-588)4040708-1 gnd Methode (DE-588)4038971-6 gnd |
| subject_GND | (DE-588)4129903-6 (DE-588)4040708-1 (DE-588)4038971-6 (DE-588)4151278-9 |
| title | Applied regression analysis and other multivariable methods |
| title_auth | Applied regression analysis and other multivariable methods |
| title_exact_search | Applied regression analysis and other multivariable methods |
| title_exact_search_txtP | Applied regression analysis and other multivariable methods |
| title_full | Applied regression analysis and other multivariable methods David G. Kleinbaum ... |
| title_fullStr | Applied regression analysis and other multivariable methods David G. Kleinbaum ... |
| title_full_unstemmed | Applied regression analysis and other multivariable methods David G. Kleinbaum ... |
| title_short | Applied regression analysis and other multivariable methods |
| title_sort | applied regression analysis and other multivariable methods |
| topic | Multivariate analysis Regression analysis Regressionsanalyse (DE-588)4129903-6 gnd Multivariate Analyse (DE-588)4040708-1 gnd Methode (DE-588)4038971-6 gnd |
| topic_facet | Multivariate analysis Regression analysis Regressionsanalyse Multivariate Analyse Methode Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016720393&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kleinbaumdavidg appliedregressionanalysisandothermultivariablemethods |