Iain Pardoe

Applied Regression Modeling 2e

• Gebundenes Buch

Produktbeschreibung

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.

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Praise for the First Edition

"The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." --The American Statistician

Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications.

The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling.

In addition, the Second Edition features clarification and expansion of challenging topics, such as:
Transformations, indicator variables, and interaction
Testing model assumptions
Nonconstant variance
Autocorrelation
Variable selection methods
Model building and graphical interpretation

Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos.

With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.

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Produktdetails

• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 346
• Erscheinungstermin: Juli 2012
• Englisch
• Abmessung: 260mm x 183mm x 23mm
• Gewicht: 848g
• ISBN-13: 9781118097281
• ISBN-10: 1118097289
• Artikelnr.: 35063040

Autorenporträt

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.

Inhaltsangabe

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