Applied Regression Modeling

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John Wiley & Sons, Jan 7, 2013 - Mathematics - 346 pages
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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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Contents

Cover
Foundations
Problems
Simple linear regression
Multiple linear regression
Regression model building I
Regression model building II
Case studies
Extensions
Computer software help
Notation and formulas
Answers for selected problems
References

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About the author (2013)

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.

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