A comprehensive guide to statistical hypothesis testing with
examples in SAS and R
When analyzing datasets the following questions often
arise:
Is there a short hand procedure for a statistical test available in
SAS or R?
If so, how do I use it?
If not, how do I program the test myself?
This book answers these questions and provides an overview of
the most common
statistical test problems in a comprehensive way, making it easy to
find and perform
an appropriate statistical test.
A general summary of statistical test theory is presented, along
with a basic
description for each test, including the necessary prerequisites,
assumptions, the
formal test problem and the test statistic. Examples in both SAS
and R are provided,
along with program code to perform the test, resulting output and
remarks
explaining the necessary program parameters.
Key features:
* Provides examples in both SAS and R for each test
presented.
* Looks at the most common statistical tests, displayed in a
clear and easy to follow way.
* Supported by a supplementary website href=’http://www.d-taeger.de/’>http://www.d-taeger.de featuring
example
program code.
Academics, practitioners and SAS and R programmers will find
this book a valuable
resource. Students using SAS and R will also find it an excellent
choice for reference
and data analysis.
Table des matières
Preface xiii
Part I INTRODUCTION 1
1 Statistical hypothesis testing 3
1.1 Theory of statistical hypothesis testing 3
1.2 Testing statistical hypothesis with SAS and R 4
1.3 Presentation of the statistical tests 13
References 15
Part II NORMAL DISTRIBUTION 17
2 Tests on the mean 19
2.1 One-sample tests 19
2.2 Two-sample tests 23
References 35
3 Tests on the variance 36
3.1 One-sample tests 36
3.2 Two-sample tests 41
References 47
Part III BINOMIAL DISTRIBUTION 49
4 Tests on proportions 51
4.1 One-sample tests 51
4.2 Two-sample tests 55
4.3 K-sample tests 62
References 64
Part IV OTHER DISTRIBUTIONS 65
5 Poisson distribution 67
5.1 Tests on the Poisson parameter 67
References 75
6 Exponential distribution 76
6.1 Test on the parameter of an exponential distribution 76
Reference 78
Part V CORRELATION 79
7 Tests on association 81
7.1 One-sample tests 81
7.2 Two-sample tests 94
References 98
Part VI NONPARAMETRIC TESTS 99
8 Tests on location 101
8.1 One-sample tests 101
8.2 Two-sample tests 110
8.3 K-sample tests 116
References 118
9 Tests on scale difference 120
9.1 Two-sample tests 120
References 131
10 Other tests 132
10.1 Two-sample tests 132
References 135
Part VII GOODNESS-OF-FIT TESTS 137
11 Tests on normality 139
11.1 Tests based on the EDF 139
11.2 Tests not based on the EDF 148
References 152
12 Tests on other distributions 154
12.1 Tests based on the EDF 154
12.2 Tests not based on the EDF 164
References 166
Part VIII TESTS ON RANDOMNESS 167
13 Tests on randomness 169
13.1 Run tests 169
13.2 Successive difference tests 178
References 185
Part IX TESTS ON CONTINGENCY TABLES 187
14 Tests on contingency tables 189
14.1 Tests on independence and homogeneity 189
14.2 Tests on agreement and symmetry 197
14.3 Test on risk measures 205
References 214
Part X TESTS ON OUTLIERS 217
15 Tests on outliers 219
15.1 Outliers tests for Gaussian null distribution 219
15.2 Outlier tests for other null distributions 229
References 235
Part XI TESTS IN REGRESSION ANALYSIS 237
16 Tests in regression analysis 239
16.1 Simple linear regression 239
16.2 Multiple linear regression 246
References 252
17 Tests in variance analysis 253
17.1 Analysis of variance 253
17.2 Tests for homogeneity of variances 258
References 263
Appendix A Datasets 264
Appendix B Tables 271
Glossary 284
Index 287
A propos de l’auteur
Dirk Taeger, Institute for Prevention and Occupational
Medicine of the German Social
Accident Insurance, Institute of the Ruhr-Universität
Bochum (IPA), Bochum, Germany
Sonja Kuhnt, Department of Computer Science, Dortmund
University of Applied Sciences
and Arts, Dortmund, Germany
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