|  | Pardoe, Iain
Applied Regression Modeling
 2. Edition August 2012
95.90 Euro
2012. 346 Pages, Hardcover
ISBN 978-1-118-09728-1 - John Wiley & Sons
|
| Buy now   
E-Books are also available on all known E-Book shops.
|
| Short description
This revised and updated book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The new edition's approach relies on the abundant use of illustrations, examples, case studies, and graphics, as well as major statistical software packages, including SPSS, Minitab, SAS, R, and R/S-PLUS. Detailed instructions for use of these packages, as well as for Microsoft Office Excel, are provided on a specially prepared and maintained author Web site.
From the contents
Preface xi
Acknowledgments xvii
Introduction xvii
1.1 Statistics in practice xvii
1.2 Learning statistics xix
1. Foundations 1
1.1 Identifying and summarizing data 1
1.2 Population distributions 5
1.3 Selecting individuals at random--probability 9
1.4 Random sampling 11
1.5 Interval estimation 15
1.6 Hypothesis testing 19
1.7 Random errors and prediction 25
1.8 Chapter summary 28
Problems 29
2. Simple linear regression 35
2.1 Probability model for X and Y 35
2.2 Least squares criterion 40
2.3 Model evaluation 45
2.4 Model assumptions 59
2.5 Model interpretation 66
2.6 Estimation and prediction 68
2.7 Chapter summary 72
Problems 78
3. Multiple linear regression 83
3.1 Probability model for (X1; X2; : : : ) and Y 83
3.2 Least squares criterion 87
3.3 Model evaluation 92
3.4 Model assumptions 118
3.5 Model interpretation 124
3.6 Estimation and prediction 126
3.7 Chapter summary 130
Problems 132
4. Regression model building I 137
4.1 Transformations 138
4.2 Interactions 159
4.3 Qualitative predictors 166
4.4 Chapter summary 182
Problems 184
5. Regression model building II 189
5.1 Influential points 189
5.2 Regression pitfalls 199
5.3 Model building guidelines 218
5.4 Model selection 221
5.5 Model interpretation using graphics 224
5.6 Chapter summary 231
Problems 234
6. Case studies 243
6.1 Home prices 243
6.2 Vehicle fuel efficiency 253
6.3 Pharmaceutical patches 261
7. Extensions 267
7.1 Generalized linear models 268
7.2 Discrete choice models 275
7.3 Multilevel models 278
7.4 Bayesian modeling 280
Appendix A. Computer software help 285
Appendix B. Critical values for t distributions 289
Appendix C. Notation and formulas 293
Appendix D. Mathematics refresher 297
Appendix E. Answers to selected problems 299
References 309
Glossary 315
Index 321
|
|
|
|
