325 pages, b/w illustrations, tables
This 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 book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes 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. Select software output appears throughout the text.
To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests).
1. Foundations 1
2. Simple linear regression 35
3. Multiple linear regression 83
4. Regression model building I 137
5. Regression model building II 189
6. Case studies 243
7. Extensions 267
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
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Iain Pardoe, PhD, is an independent consultant and also serves on the faculty of mathematics and statistics at Thompson Rivers University, Canada. He has published extensively in his areas of research interest, which include Bayesian analysis, multilevel modeling, graphical methods, and statistics education.