Statistics and data with R: an applied approach through examples
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester
Wiley
2008
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. [563] - 568 Includes bibliographical references and index |
| Physical Description: | XVIII, 599 S. graph. Darst. |
| ISBN: | 9780470758052 |
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MARC
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| 020 | |a 9780470758052 |c cloth |9 978-0-470-75805-2 | ||
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| 040 | |a DE-604 |b ger |e aacr | ||
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| 084 | |a ST 250 |0 (DE-625)143626: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Cohen, Yosef |d 1946- |e Verfasser |0 (DE-588)111404487 |4 aut | |
| 245 | 1 | 0 | |a Statistics and data with R |b an applied approach through examples |c Yosef Cohen ; Jeremiah Y. Cohen |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2008 | |
| 300 | |a XVIII, 599 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Literaturverz. S. [563] - 568 | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Mathematical statistics |x Data processing | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Cohen, Jeremiah Y. |e Verfasser |0 (DE-588)136682464 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016766257&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016766257 | ||
Record in the Search Index
| _version_ | 1804138058959814656 |
|---|---|
| adam_text | Contents
Preface
xv
Part I Data in statistics and
R
1
Basic
R
3
1.1
Preliminaries
4
1.1.1
An
R
session
4
1.1.2
Editing statements
8
1.1.3
The functions help
(),
help.search
()
and example
() 8
1.1.4
Expressions
10
1.1.5
Comments, line continuation and Esc
11
1.1.6
source
(),
sink
()
and history
() 11
1.2
Modes
13
1.3
Vectors
14
1.3.1
Creating vectors
14
1.3.2
Useful vector functions
15
1.3.3
Vector arithmetic
15
1.3.4
Character vectors
17
1.3.5
Subsets and index vectors
18
1.4
Arithmetic operators and special values
20
1.4.1
Arithmetic operators
20
1.4.2
Logical operators
21
1.4.3
Special values
22
1.5
Objects
24
1.5.1
Orientation
24
1.5.2
Object attributes
26
1.6
Programming
28
1.6.1
Execution controls
28
1.6.2
Functions
30
1.7
Packages
33
viii Contents
1.8
Graphics
34
1.8.1
High-level plotting functions
35
1.8.2
Low-level plotting functions
36
1.8.3
Interactive plotting functions
36
1.8.4
Dynamic plotting
36
1.9
Customizing the workspace
36
1.10
Projects
37
1.11
A note about producing figures and output
39
1.11.1
opengO
39
1.11.2
savegO
40
1.11.3
h()
40
1.11.4
nqdO
40
1.12
Assignments
41
2
Data in statistics and in
R
45
2.1
Types of data
45
2.1.1
Factors
45
2.1.2
Ordered factors
48
2.1.3
Numerical variables
49
2.1.4
Character variables
50
2.1.5
Dates in
R
50
2.2
Objects that hold data
50
2.2.1
Arrays and matrices
51
2.2.2
Lists
52
2.2.3
Data frames
54
2.3
Data organization
55
2.3.1
Data tables
55
2.3.2
Relationships among tables
57
2.4
Data import, export and connections
58
2.4.1
Import and export
58
2.4.2
Data connections
60
2.5
Data manipulation
63
2.5.1
Flat tables and expand tables
63
2.5.2
Stack, unstack and reshape
64
2.5.3
Split, unsplit and unlist
66
2.5.4
Cut
66
2.5.5
Merge, union and intersect
68
2.5.6
is.
elementi)
69
2.6
Manipulating strings
71
2.7
Assignments
72
3
Presenting data
75
3.1
Tables and the flavors of apply
() 75
3.2
Bar plots
77
3.3
Histograms
81
3.4
Dot charts
85
3.5
Scatter plots
86
3.6
Lattice plots
88
Contents ix
3.7
Three-dimensional plots and contours
90
3.8
Assignments
90
Part II Probability, densities and distributions
4
Probability and random variables
97
4.1
Set theory
98
4.1.1
Sets and algebra of sets
98
4.1.2
Set theory in
R
103
4.2
Trials, events and experiments
103
4.3
Definitions and properties of probability
108
4.3.1
Definitions of probability
108
4.3.2
Properties of probability 111
4.3.3
Equally likely events
112
4.3.4
Probability and set theory
112
4.4
Conditional probability and independence
113
4.4.1
Conditional probability
114
4.4.2
Independence
116
4.5
Algebra with probabilities
118
4.5.1
Sampling with and without replacement
118
4.5.2
Addition
119
4.5.3
Multiplication
120
4.5.4
Counting rules
120
4.6
Random variables
127
4.7
Assignments
128
5
Discrete densities and distributions
137
5.1
Densities
137
5.2
Distributions
141
5.3
Properties
143
5.3.1
Densities
144
5.3.2
Distributions
144
5.4
Expected values
144
5.5
Variance and standard deviation
146
5.6
The binomial 147
5.6.1
Expectation and variance
151
5.6.2
Decision making with the binomial
151
5.7
The
Poisson 153
5.7.1
The
Poisson
approximation to the binomial
155
5.7.2
Expectation and variance
156
5.7.3
Variance of the
Poisson
density
157
5.8
Estimating parameters
161
5.9
Some useful discrete densities
163
5.9.1
Multinomial 163
5.9.2
Negative binomial
165
5.9.3
Hypergeometric
168
5.10
Assignments
l I
Contents
Continuous distributions and densities
177
6.1
Distributions !77
6.2
Densities 18°
6.3
Properties 181
6.3.1
Distributions 181
6.3.2
Densities
182
6.4
Expected values I8»*
6.5
Variance and standard deviation
184
6.6
Areas under density curves
185
6.7
Inverse distributions and simulations
187
6.8
Some useful continuous densities
189
6.8.1
Double exponential (Laplace)
189
6.8.2
Normal
191
6.8.3
χ2
193
6.8.4
Student-í
195
6.8.5
F
197
6.8.6 Lognormal 198
6.8.7
Gamma
199
6.8.8
Beta
201
6.9
Assignments
203
The normal and sampling densities
205
7.1
The normal density
205
7.1.1
The standard normal
207
7.1.2
Arbitrary normal
210
7.1.3
Expectation and variance of the normal
212
7.2
Applications of the normal
213
7.2.1
The normal approximation of discrete densities
214
7.2.2
Normal approximation to the binomial
215
7.2.3
The normal approximation to the
Poisson
218
7.2.4
Testing for normality
220
7.3
Data transformations
225
7.4
Random samples and sampling densities
226
7.4.1
Random samples
227
7.4.2
Sampling densities
228
7.5
A detour: using
R
efficiently
230
7.5.1
Avoiding loops
230
7.5.2
Timing execution
230
7.6
The sampling density of the mean
232
7.6.1
The central limit theorem
232
7.6.2
The sampling density
232
7.6.3
Consequences of the central limit theorem
234
7.7
The sampling density of proportion
235
7.7.1
The sampling density
236
7.7.2
Consequence of the central limit theorem
238
7.8
The sampling density of intensity
239
7.8.1
The sampling density
239
Contents xi
7.8.2
Consequences of the central limit theorem
241
7.9
The sampling density of variance
241
7.10
Bootstrap: arbitrary parameters of arbitrary densities
242
7.11
Assignments
243
Part III Statistics
8
Exploratory data analysis
251
8.1
Graphical methods
252
8.2
Numerical summaries
253
8.2.1
Measures of the center of the data
253
8.2.2
Measures of the spread of data
261
8.2.3
The Chebyshev and empirical rules
267
8.2.4
Measures of association between variables
269
8.3
Visual summaries
275
8.3.1
Box plots
275
8.3.2
Lag plots
276
8.4
Assignments
277
9
Point and interval estimation
283
9.1
Point estimation
284
9.1.1
Maximum likelihood estimators
284
9.1.2
Desired properties of point estimators
285
9.1.3
Point estimates for useful densities
288
9.1.4
Point estimate of population variance
292
9.1.5
Finding MLE numerically
293
9.2
Interval estimation
294
9.2.1
Large sample confidence intervals
295
9.2.2
Small sample confidence intervals
301
9.3
Point and interval estimation for arbitrary densities
304
9.4
Assignments
307
10
Single sample hypotheses testing
313
10.1
Null and alternative hypotheses
313
10.1.1
Formulating hypotheses
314
10.1.2
Types of errors in hypothesis testing
316
10.1.3
Choosing a significance level
317
10.2
Large sample hypothesis testing
318
10.2.1
Means
318
10.2.2
Proportions
323
10.2.3
Intensities
324
10.2.4
Common sense significance
325
10.3
Small sample hypotheses testing
326
10.3.1
Means
326
10.3.2
Proportions
327
10.3.3
Intensities
328
xii Contents
10.4
Arbitrary statistics of arbitrary densities
329
10.5
p-values
330
10.6
Assignments
333
11
Power and sample size for single samples
341
11.1
Large sample
341
11.1.1
Means
342
11.1.2
Proportions
352
11.1.3
Intensities
356
11.2
Small samples
359
11.2.1
Means
359
11.2.2
Proportions
361
11.2.3
Intensities
363
11.3
Power and sample size for arbitrary densities
365
11.4
Assignments
365
12
Two samples
369
12.1
Large samples
370
12.1.1
Means
370
12.1.2
Proportions
375
12.1.3
Intensities
379
12.2
Small samples
380
12.2.1
Estimating variance and standard error
380
12.2.2
Hypothesis testing and confidence intervals for variance
382
12.2.3
Means
384
12.2.4
Proportions
386
12.2.5
Intensities
387
12.3
Unknown densities
388
12.3.1
Rank sum test
389
12.3.2
t
vs. rank sum
392
12.3.3
Signed rank test
392
12.3.4
Bootstrap
394
12.4
Assignments
396
13
Power and sample size for two samples
401
13.1
Two means from normal populations
401
13.1.1
Power
401
13.1.2
Sample size
404
13.2
Two proportions
406
13.2.1
Power
407
13.2.2
Sample size
409
13.3
Two rates
410
13.4
Assignments
415
14
Simple linear regression
417
14.1
Simple linear models
417
14.1.1
The regression line
418
14.1.2
Interpretation of simple linear models
419
Contents xiii
14.2
Estimating regression coefficients
422
14.3
The model goodness of fit
428
14.3.1
The
F
test
428
14.3.2
The correlation coefficient
433
14.3.3
The correlation coefficient vs. the slope
434
14.4
Hypothesis testing and confidence intervals
434
14.4.1
ŕ-test
for model coefficients
435
14.4.2
Confidence intervals for model coefficients
435
14.4.3
Confidence intervals for model predictions
436
14.4.4
i-test for the correlation coefficient
438
14.4.5
z
tests for the correlation coefficient
439
14.4.6
Confidence intervals for the correlation coefficient
441
14.5
Model assumptions
442
14.6
Model diagnostics
443
14.6.1
The hat matrix
445
14.6.2
Standardized residuals
447
14.6.3
Studentized residuals
448
14.6.4
The RSTUDENT residuals
449
14.6.5
The DFFITS residuals
453
14.6.6
The DFBETAS residuals
454
14.6.7
Cooke s distance
456
14.6.8
Conclusions
457
14.7
Power and sample size for the correlation coefficient
458
14.8
Assignments
459
15
Analysis of variance
463
15.1
One-way, fixed-effects ANOVA
463
15.1.1
The model and assumptions
464
15.1.2
The F-test
469
15.1.3
Paired group comparisons
475
15.1.4
Comparing sets of groups
484
15.2
Non-parametric one-way ANOVA
488
15.2.1
The
Kruskal-Waľiis
test
488
15.2.2
Multiple comparisons
491
15.3
One-way, random-effects ANOVA
492
15.4
Two-way ANOVA
495
15.4.1
Two-way, fixed-effects ANOVA
496
15.4.2
The model and assumptions
496
15.4.3
Hypothesis testing and the F-test
500
15.5
Two-way linear mixed effects models
505
15.6
Assignments
509
16
Simple logistic regression
511
16.1
Simple binomial logistic regression
511
16.2
Fitting and selecting models
519
16.2.1
The log likelihood function
519
16.2.2
Standard errors of coefficients and predictions
521
16.2.3
Nested models 524
xiv Contents
16.3
Assessing goodness of fit
525
16.3.1
The Pearson
χ2
statistic
526
16.3.2
The deviance
χ2
statistic
527
16.3.3
The group adjusted
χ2
statistic
528
16.3.4
The ROC curve
529
16.4
Diagnostics
533
16.4.1
Analysis of residuals
533
16.4.2
Validation
536
16.4.3
Applications of simple logistic regression to
2
χ
2
tables
536
16.5
Assignments
539
17
Application: the shape of wars to come
541
17.1
A statistical profile of the war in Iraq
541
17.1.1
Introduction
542
17.1.2
The data
542
17.1.3
Results
543
17.1.4
Conclusions
550
17.2
A statistical profile of the second Intifada
552
17.2.1
Introduction
552
17.2.2
The data
553
17.2.3
Results
553
17.2.4
Conclusions
561
References
563
R
Index
569
General Index
583
|
| adam_txt |
Contents
Preface
xv
Part I Data in statistics and
R
1
Basic
R
3
1.1
Preliminaries
4
1.1.1
An
R
session
4
1.1.2
Editing statements
8
1.1.3
The functions help
(),
help.search
()
and example
() 8
1.1.4
Expressions
10
1.1.5
Comments, line continuation and Esc
11
1.1.6
source
(),
sink
()
and history
() 11
1.2
Modes
13
1.3
Vectors
14
1.3.1
Creating vectors
14
1.3.2
Useful vector functions
15
1.3.3
Vector arithmetic
15
1.3.4
Character vectors
17
1.3.5
Subsets and index vectors
18
1.4
Arithmetic operators and special values
20
1.4.1
Arithmetic operators
20
1.4.2
Logical operators
21
1.4.3
Special values
22
1.5
Objects
24
1.5.1
Orientation
24
1.5.2
Object attributes
26
1.6
Programming
28
1.6.1
Execution controls
28
1.6.2
Functions
30
1.7
Packages
33
viii Contents
1.8
Graphics
34
1.8.1
High-level plotting functions
35
1.8.2
Low-level plotting functions
36
1.8.3
Interactive plotting functions
36
1.8.4
Dynamic plotting
36
1.9
Customizing the workspace
36
1.10
Projects
37
1.11
A note about producing figures and output
39
1.11.1
opengO
39
1.11.2
savegO
40
1.11.3
h()
40
1.11.4
nqdO
40
1.12
Assignments
41
2
Data in statistics and in
R
45
2.1
Types of data
45
2.1.1
Factors
45
2.1.2
Ordered factors
48
2.1.3
Numerical variables
49
2.1.4
Character variables
50
2.1.5
Dates in
R
50
2.2
Objects that hold data
50
2.2.1
Arrays and matrices
51
2.2.2
Lists
52
2.2.3
Data frames
54
2.3
Data organization
55
2.3.1
Data tables
55
2.3.2
Relationships among tables
57
2.4
Data import, export and connections
58
2.4.1
Import and export
58
2.4.2
Data connections
60
2.5
Data manipulation
63
2.5.1
Flat tables and expand tables
63
2.5.2
Stack, unstack and reshape
64
2.5.3
Split, unsplit and unlist
66
2.5.4
Cut
66
2.5.5
Merge, union and intersect
68
2.5.6
is.
elementi)
69
2.6
Manipulating strings
71
2.7
Assignments
72
3
Presenting data
75
3.1
Tables and the flavors of apply
() 75
3.2
Bar plots
77
3.3
Histograms
81
3.4
Dot charts
85
3.5
Scatter plots
86
3.6
Lattice plots
88
Contents ix
3.7
Three-dimensional plots and contours
90
3.8
Assignments
90
Part II Probability, densities and distributions
4
Probability and random variables
97
4.1
Set theory
98
4.1.1
Sets and algebra of sets
98
4.1.2
Set theory in
R
103
4.2
Trials, events and experiments
103
4.3
Definitions and properties of probability
108
4.3.1
Definitions of probability
108
4.3.2
Properties of probability 111
4.3.3
Equally likely events
112
4.3.4
Probability and set theory
112
4.4
Conditional probability and independence
113
4.4.1
Conditional probability
114
4.4.2
Independence
116
4.5
Algebra with probabilities
118
4.5.1
Sampling with and without replacement
118
4.5.2
Addition
119
4.5.3
Multiplication
120
4.5.4
Counting rules
120
4.6
Random variables
127
4.7
Assignments
128
5
Discrete densities and distributions
137
5.1
Densities
137
5.2
Distributions
141
5.3
Properties
143
5.3.1
Densities
144
5.3.2
Distributions
144
5.4
Expected values
144
5.5
Variance and standard deviation
146
5.6
The binomial 147
5.6.1
Expectation and variance
151
5.6.2
Decision making with the binomial
151
5.7
The
Poisson 153
5.7.1
The
Poisson
approximation to the binomial
155
5.7.2
Expectation and variance
156
5.7.3
Variance of the
Poisson
density
157
5.8
Estimating parameters
161
5.9
Some useful discrete densities
163
5.9.1
Multinomial 163
5.9.2
Negative binomial
165
5.9.3
Hypergeometric
168
5.10
Assignments
l'I
Contents
Continuous distributions and densities
177
6.1
Distributions !77
6.2
Densities 18°
6.3
Properties 181
6.3.1
Distributions 181
6.3.2
Densities
182
6.4
Expected values I8»*
6.5
Variance and standard deviation
184
6.6
Areas under density curves
185
6.7
Inverse distributions and simulations
187
6.8
Some useful continuous densities
189
6.8.1
Double exponential (Laplace)
189
6.8.2
Normal
191
6.8.3
χ2
193
6.8.4
Student-í
195
6.8.5
F
197
6.8.6 Lognormal 198
6.8.7
Gamma
199
6.8.8
Beta
201
6.9
Assignments
203
The normal and sampling densities
205
7.1
The normal density
205
7.1.1
The standard normal
207
7.1.2
Arbitrary normal
210
7.1.3
Expectation and variance of the normal
212
7.2
Applications of the normal
213
7.2.1
The normal approximation of discrete densities
214
7.2.2
Normal approximation to the binomial
215
7.2.3
The normal approximation to the
Poisson
218
7.2.4
Testing for normality
220
7.3
Data transformations
225
7.4
Random samples and sampling densities
226
7.4.1
Random samples
227
7.4.2
Sampling densities
228
7.5
A detour: using
R
efficiently
230
7.5.1
Avoiding loops
230
7.5.2
Timing execution
230
7.6
The sampling density of the mean
232
7.6.1
The central limit theorem
232
7.6.2
The sampling density
232
7.6.3
Consequences of the central limit theorem
234
7.7
The sampling density of proportion
235
7.7.1
The sampling density
236
7.7.2
Consequence of the central limit theorem
238
7.8
The sampling density of intensity
239
7.8.1
The sampling density
239
Contents xi
7.8.2
Consequences of the central limit theorem
241
7.9
The sampling density of variance
241
7.10
Bootstrap: arbitrary parameters of arbitrary densities
242
7.11
Assignments
243
Part III Statistics
8
Exploratory data analysis
251
8.1
Graphical methods
252
8.2
Numerical summaries
253
8.2.1
Measures of the center of the data
253
8.2.2
Measures of the spread of data
261
8.2.3
The Chebyshev and empirical rules
267
8.2.4
Measures of association between variables
269
8.3
Visual summaries
275
8.3.1
Box plots
275
8.3.2
Lag plots
276
8.4
Assignments
277
9
Point and interval estimation
283
9.1
Point estimation
284
9.1.1
Maximum likelihood estimators
284
9.1.2
Desired properties of point estimators
285
9.1.3
Point estimates for useful densities
288
9.1.4
Point estimate of population variance
292
9.1.5
Finding MLE numerically
293
9.2
Interval estimation
294
9.2.1
Large sample confidence intervals
295
9.2.2
Small sample confidence intervals
301
9.3
Point and interval estimation for arbitrary densities
304
9.4
Assignments
307
10
Single sample hypotheses testing
313
10.1
Null and alternative hypotheses
313
10.1.1
Formulating hypotheses
314
10.1.2
Types of errors in hypothesis testing
316
10.1.3
Choosing a significance level
317
10.2
Large sample hypothesis testing
318
10.2.1
Means
318
10.2.2
Proportions
323
10.2.3
Intensities
324
10.2.4
Common sense significance
325
10.3
Small sample hypotheses testing
326
10.3.1
Means
326
10.3.2
Proportions
327
10.3.3
Intensities
328
xii Contents
10.4
Arbitrary statistics of arbitrary densities
329
10.5
p-values
330
10.6
Assignments
333
11
Power and sample size for single samples
341
11.1
Large sample
341
11.1.1
Means
342
11.1.2
Proportions
352
11.1.3
Intensities
356
11.2
Small samples
359
11.2.1
Means
359
11.2.2
Proportions
361
11.2.3
Intensities
363
11.3
Power and sample size for arbitrary densities
365
11.4
Assignments
365
12
Two samples
369
12.1
Large samples
370
12.1.1
Means
370
12.1.2
Proportions
375
12.1.3
Intensities
379
12.2
Small samples
380
12.2.1
Estimating variance and standard error
380
12.2.2
Hypothesis testing and confidence intervals for variance
382
12.2.3
Means
384
12.2.4
Proportions
386
12.2.5
Intensities
387
12.3
Unknown densities
388
12.3.1
Rank sum test
389
12.3.2
t
vs. rank sum
392
12.3.3
Signed rank test
392
12.3.4
Bootstrap
394
12.4
Assignments
396
13
Power and sample size for two samples
401
13.1
Two means from normal populations
401
13.1.1
Power
401
13.1.2
Sample size
404
13.2
Two proportions
406
13.2.1
Power
407
13.2.2
Sample size
409
13.3
Two rates
410
13.4
Assignments
415
14
Simple linear regression
417
14.1
Simple linear models
417
14.1.1
The regression line
418
14.1.2
Interpretation of simple linear models
419
Contents xiii
14.2
Estimating regression coefficients
422
14.3
The model goodness of fit
428
14.3.1
The
F
test
428
14.3.2
The correlation coefficient
433
14.3.3
The correlation coefficient vs. the slope
434
14.4
Hypothesis testing and confidence intervals
434
14.4.1
ŕ-test
for model coefficients
435
14.4.2
Confidence intervals for model coefficients
435
14.4.3
Confidence intervals for model predictions
436
14.4.4
i-test for the correlation coefficient
438
14.4.5
z
tests for the correlation coefficient
439
14.4.6
Confidence intervals for the correlation coefficient
441
14.5
Model assumptions
442
14.6
Model diagnostics
443
14.6.1
The hat matrix
445
14.6.2
Standardized residuals
447
14.6.3
Studentized residuals
448
14.6.4
The RSTUDENT residuals
449
14.6.5
The DFFITS residuals
453
14.6.6
The DFBETAS residuals
454
14.6.7
Cooke's distance
456
14.6.8
Conclusions
457
14.7
Power and sample size for the correlation coefficient
458
14.8
Assignments
459
15
Analysis of variance
463
15.1
One-way, fixed-effects ANOVA
463
15.1.1
The model and assumptions
464
15.1.2
The F-test
469
15.1.3
Paired group comparisons
475
15.1.4
Comparing sets of groups
484
15.2
Non-parametric one-way ANOVA
488
15.2.1
The
Kruskal-Waľiis
test
488
15.2.2
Multiple comparisons
491
15.3
One-way, random-effects ANOVA
492
15.4
Two-way ANOVA
495
15.4.1
Two-way, fixed-effects ANOVA
496
15.4.2
The model and assumptions
496
15.4.3
Hypothesis testing and the F-test
500
15.5
Two-way linear mixed effects models
505
15.6
Assignments
509
16
Simple logistic regression
511
16.1
Simple binomial logistic regression
511
16.2
Fitting and selecting models
519
16.2.1
The log likelihood function
519
16.2.2
Standard errors of coefficients and predictions
521
16.2.3
Nested models 524
xiv Contents
16.3
Assessing goodness of fit
525
16.3.1
The Pearson
χ2
statistic
526
16.3.2
The deviance
χ2
statistic
527
16.3.3
The group adjusted
χ2
statistic
528
16.3.4
The ROC curve
529
16.4
Diagnostics
533
16.4.1
Analysis of residuals
533
16.4.2
Validation
536
16.4.3
Applications of simple logistic regression to
2
χ
2
tables
536
16.5
Assignments
539
17
Application: the shape of wars to come
541
17.1
A statistical profile of the war in Iraq
541
17.1.1
Introduction
542
17.1.2
The data
542
17.1.3
Results
543
17.1.4
Conclusions
550
17.2
A statistical profile of the second Intifada
552
17.2.1
Introduction
552
17.2.2
The data
553
17.2.3
Results
553
17.2.4
Conclusions
561
References
563
R
Index
569
General Index
583 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Cohen, Yosef 1946- Cohen, Jeremiah Y. |
| author_GND | (DE-588)111404487 (DE-588)136682464 |
| author_facet | Cohen, Yosef 1946- Cohen, Jeremiah Y. |
| author_role | aut aut |
| author_sort | Cohen, Yosef 1946- |
| author_variant | y c yc j y c jy jyc |
| building | Verbundindex |
| bvnumber | BV035098238 |
| callnumber-first | Q - Science |
| callnumber-label | QA276 |
| callnumber-raw | QA276.45.R3 |
| callnumber-search | QA276.45.R3 |
| callnumber-sort | QA 3276.45 R3 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SK 450 SK 850 ST 250 ST 601 |
| ctrlnum | (OCoLC)235946051 (DE-599)BVBBV035098238 |
| dewey-full | 519.50285/2133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.50285/2133 |
| dewey-search | 519.50285/2133 |
| dewey-sort | 3519.50285 42133 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV035098238 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:13:11Z |
| indexdate | 2024-07-09T21:22:10Z |
| institution | BVB |
| isbn | 9780470758052 |
| language | English |
| lccn | 2008032153 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016766257 |
| oclc_num | 235946051 |
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| owner_facet | DE-898 DE-BY-UBR DE-703 DE-521 DE-11 DE-824 DE-573 |
| physical | XVIII, 599 S. graph. Darst. |
| publishDate | 2008 |
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| publisher | Wiley |
| record_format | marc |
| spelling | Cohen, Yosef 1946- Verfasser (DE-588)111404487 aut Statistics and data with R an applied approach through examples Yosef Cohen ; Jeremiah Y. Cohen 1. publ. Chichester Wiley 2008 XVIII, 599 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. [563] - 568 Includes bibliographical references and index Datenverarbeitung Mathematical statistics Data processing R (Computer program language) R Programm (DE-588)4705956-4 gnd rswk-swf R Programm (DE-588)4705956-4 s DE-604 Cohen, Jeremiah Y. Verfasser (DE-588)136682464 aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016766257&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cohen, Yosef 1946- Cohen, Jeremiah Y. Statistics and data with R an applied approach through examples Datenverarbeitung Mathematical statistics Data processing R (Computer program language) R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4705956-4 |
| title | Statistics and data with R an applied approach through examples |
| title_auth | Statistics and data with R an applied approach through examples |
| title_exact_search | Statistics and data with R an applied approach through examples |
| title_exact_search_txtP | Statistics and data with R an applied approach through examples |
| title_full | Statistics and data with R an applied approach through examples Yosef Cohen ; Jeremiah Y. Cohen |
| title_fullStr | Statistics and data with R an applied approach through examples Yosef Cohen ; Jeremiah Y. Cohen |
| title_full_unstemmed | Statistics and data with R an applied approach through examples Yosef Cohen ; Jeremiah Y. Cohen |
| title_short | Statistics and data with R |
| title_sort | statistics and data with r an applied approach through examples |
| title_sub | an applied approach through examples |
| topic | Datenverarbeitung Mathematical statistics Data processing R (Computer program language) R Programm (DE-588)4705956-4 gnd |
| topic_facet | Datenverarbeitung Mathematical statistics Data processing R (Computer program language) R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016766257&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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