A handbook of statistical analyses using R

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... FS Library	32080000000179	QA276.45.R3 E94 2014	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010337179	QA276.45.R3 E94 2014	Open Access Book	Book	Searching... Unknown

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.

New to the Third Edition

Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses

Whether you're a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.

Preface to Third Edition	p. xi
Preface to Second Edition	p. xiii
Preface to First Edition	p. xv
1 An Introduction to R	p. 1
1.1 What is R?	p. 1
1.2 Installing R	p. 2
1.3 Help and Documentation	p. 4
1.4 Data Objects in R	p. 5
1.5 Data Import and Export	p. 9
1.6 Basic Data Manipulation	p. 11
1.7 Computing with Data	p. 14
1.8 Organizing an Analysis	p. 20
1.9 Summary of Findings	p. 21
1.10 Final Comments	p. 23
2 Data Analysis Using Graphical Displays	p. 25
2.1 Introduction	p. 25
2.2 Initial Data Analysis	p. 27
2.3 Analysis Using R	p. 29
2.4 Summary of Findings	p. 40
2.5 Final Comments	p. 41
3 Simple Inference	p. 47
3.1 Introduction	p. 47
3.2 Statistical Tests	p. 51
3.3 Analysis Using R	p. 55
3.4 Summary of Findings	p. 65
3.5 Final Comments	p. 66
4 Conditional Inference	p. 69
4.1 Introduction	p. 69
4.2 Conditional Test Procedures	p. 72
4.3 Analysis Using R	p. 74
4.4 Summary of Findings	p. 81
4.5 Final Comments	p. 81
5 Analysis of Variance	p. 83
5.1 Introduction	p. 83
5.2 Analysis of Variance	p. 86
5.3 Analysis Using R	p. 87
5.4 Summary of Findings	p. 98
5.5 Final Comments	p. 99
6 Simple and Multiple Linear Regression	p. 101
6.1 Introduction	p. 101
6.2 Simple Linear Regression	p. 103
6.3 Multiple Linear Regression	p. 104
6.4 Analysis Using R	p. 107
6.5 Summary of Findings	p. 118
6.6 Final Comments	p. 119
7 Logistic Regression and Generalized Linear Models	p. 121
7.1 Introduction	p. 121
7.2 Logistic Regression and Generalized Linear Models	p. 124
7.3 Analysis Using R	p. 127
7.4 Summary of Findings	p. 145
7.5 Final Comments	p. 147
8 Density Estimation	p. 149
8.1 Introduction	p. 149
5.1 Density Estimation	p. 151
8.1 Analysis Using R	p. 156
8.2 Summary of Findings	p. 164
8.3 Final Comments	p. 165
9 Recursive Partitioning	p. 171
9.1 Introduction	p. 171
9.2 Recursive Partitioning	p. 174
9.3 Analysis Using R	p. 175
9.4 Summary of Findings	p. 186
9.5 Final Comments	p. 186
10 Scatterplot Smoothers and Additive Models	p. 187
10.1 Introduction	p. 187
10.2 Scatterplot Smoothers and Generalized Additive Models	p. 191
10.3 Analysis Using R	p. 196
10.4 Summary of Findings	p. 205
10.5 Final Comments	p. 206
11 Survival Analysis	p. 209
11.1 Introduction	p. 209
11.2 Survival Analysis	p. 210
11.3 Analysis Using R	p. 216
11.4 Summary of Findings	p. 223
11.5 Final Comments	p. 224
12 Quantile Regression	p. 227
12.1 Introduction	p. 227
12.2 Quantile Regression	p. 228
12.3 Analysis Using R	p. 230
12.4 Summary of Findings	p. 241
12.5 Final Comments	p. 242
13 Analyzing Longitudinal Data I	p. 243
13.1 Introduction	p. 243
13.2 Analyzing Longitudinal Data	p. 246
13.3 Linear Mixed Effects Models	p. 247
13.4 Analysis Using R	p. 249
13.5 Prediction of Random Effects	p. 253
13.6 The Problem of Dropouts	p. 253
13.7 Summary of Findings	p. 256
13.8 Final Comments	p. 258
14 Analyzing Longitudinal Data II	p. 261
14.1 Introduction	p. 261
14.2 Methods for Non-normal Distributions	p. 263
14.3 Analysis Using R: GEE	p. 268
14.4 Analysis Using R: Random Effects	p. 277
14.5 Summary of Findings	p. 280
14.6 Final Comments	p. 280
15 Simultaneous Inference and Multiple Comparisons	p. 283
15.1 Introduction	p. 283
15.2 Simultaneous Inference and Multiple Comparisons	p. 286
15.3 Analysis Using R	p. 287
15.4 Summary of Findings	p. 294
15.5 Final Comments	p. 295
16 Missing Values	p. 297
16.1 Introduction	p. 297
16.2 The Problems of Missing Data	p. 298
16.3 Dealing With Missing Values	p. 299
16.4 Imputing Missing Values	p. 300
16.5 Analyzing Multiply Imputed Data	p. 301
16.6 Analysis Using R	p. 303
16.6 Summary of Findings	p. 311
16.6 Final Comments	p. 311
17 Meta-Analysis	p. 313
17.1 Introduction	p. 313
17.2 Systematic Reviews and Meta-Analysis	p. 315
17.3 Statistics of Meta-Analysis	p. 317
17.4 Analysis Using R	p. 319
17.1 Meta-Regression	p. 322
17.1 Publication Bias	p. 323
17.2 Summary of Findings	p. 325
17.3 Final Comments	p. 325
18 Bayesian Inference	p. 331
18.1 Introduction	p. 331
18.2 Bayesian Inference	p. 333
18.1 Analysis Using R	p. 337
18.1 Summary of Findings	p. 344
18.2 Final Comments	p. 344
19 Principal Component Analysis	p. 347
19.1 Introduction	p. 347
19.2 Principal Component Analysis	p. 347
19.3 Analysis Using R	p. 350
19.4 Summary of Findings	p. 357
19.5 Final Comments	p. 357
20 Multidimensional Scaling	p. 361
20.1 Introduction	p. 361
20.2 Multidimensional Scaling	p. 361
20.3 Analysis Using R	p. 367
20.4 Summary of Findings	p. 372
20.5 Final Comments	p. 373
21 Cluster Analysis	p. 377
21.1 Introduction	p. 377
21.2 Cluster Analysis	p. 380
21.3 Analysis Using R	p. 387
21.4 Summary of Findings	p. 394
23.1 Final Comments	p. 397
Bibliography	p. 399
Index	p. 415

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