Applied regression modeling
By: Iain Pardoe
Edition: 2nd ed.
Publisher: Wiley
Published: 2012
Place of publication: Hoboken, NJ
Language: eng
ISBN-13: 9781118097281
Subjects: Regression analysis; Statistics; MATHEMATICS / Probability & Statistics / Regression Analysis
Description: "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(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), 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)"--
Notes: Includes index.
Table of contents: title: Machine generated contents note: Preface xiAcknowledgments xviiIntroduction xvii1.1 Statistics in practice xvii1.2 Learning statistics xix1. Foundations 11.1 Identifying and summarizing data 11.2 Population distributions 51.3 Selecting individuals at random; title: probability 91.4 Random sampling 111.5 Interval estimation 151.6 Hypothesis testing 191.7 Random errors and prediction 251.8 Chapter summary 28Problems 292. Simple linear regression 352.1 Probability model for X and Y 352.2 Least squares criterion 402.3 Model evaluation 452.4 Model assumptions 592.5 Model interpretation 662.6 Estimation and prediction 682.7 Chapter summary 72Problems 783. Multiple linear regression 833.1 Probability model for (X1; X2; : : : ) and Y 833.2 Least squares criterion 873.3 Model evaluation 923.4 Model assumptions 1183.5 Model interpretation 1243.6 Estimation and prediction 1263.7 Chapter summary 130Problems 1324. Regression model building I 1374.1 Transformations 1384.2 Interactions 1594.3 Qualitative predictors 1664.4 Chapter summary 182Problems 1845. Regression model building II 1895.1 Influential points 1895.2 Regression pitfalls 1995.3 Model building guidelines 2185.4 Model selection 2215.5 Model interpretation using graphics 2245.6 Chapter summary 231Problems 2346. Case studies 2436.1 Home prices 2436.2 Vehicle fuel efficiency 2536.3 Pharmaceutical patches 2617. Extensions 2677.1 Generalized linear models 2687.2 Discrete choice models 2757.3 Multilevel models 2787.4 Bayesian modeling 280Appendix A. Computer software help 285Appendix B. Critical values for t distributions 289Appendix C. Notation and formulas 293Appendix D. Mathematics refresher 297Appendix E. Answers to selected problems 299References 309Glossary 315Index 321.