Average Reviews:
(More customer reviews)This is an accessible and very well-written introductory book on linear regression that goes much deeper than most books written at its level. Compared with other similar books, it is much more thoughtful and critical. This is one of the few introductory books on regression that gets people to actually think about the material and does not set up bad habits or encourage people to proceed blindly.
The prerequisites for this book are modest. It does require some math skills, particularly, ability to manipulate summation notation effectively. Some parts of the book require linear algebra. The book has a short review of linear algebra, which is not enough to teach the subject on its own, but might be useful review for someone with prior exposure. A few sections are genuinely hard (such as the implementation of nonlinear regression) and require more math skills, but these are not central to the flow of the book and can be omitted. In terms of other background, some general life experience and familiarity with the social sciences or natural sciences would be helpful in order to appreciate the motivation behind and applications of the material.
This book offers a remarkably exhaustive a coverage of the conventional topics in regression. The choice and order of topics is natural. I particularly like the way the book includes chapters on model selection and model checking, and explores these issues in depth. These are issues that are often glossed over.
Perhaps the best aspect of this book is the interplay between mathematical theory and applications. The mathematical details are expanded in rich discussion which helps the reader to develop mathematical intuition, and these details are presented in ways that enrich the discussion of applications. This is one of the few books that presents both the mathematical and practical sides of the subject well and integrates them fully. It takes skill and thought to create a book as well-executed as this one, and I think the authors deserve credit for this!
This book would make an excellent textbook for a first course in regression. It would remain a useful reference for more advanced students. People looking for a more advanced book on the same subject might want to check out the book "Statistical Models" by Freedman, or the "Regression Analysis: A Constructive Critique" by Berk.
Lastly, I must mention, the price of this book is absurd! This book should be less than half the cost. It's not that long and it has the usual cheap glued binding of a textbook. At $122, the glued binding for this book should constitute a criminal offense.
Click Here to see more reviews about: Introduction to Regression Modeling (with CD-ROM) (Duxbury Applied)
Using a data-driven approach, this book is an exciting blend of theory and interesting regression applications. Students learn the theory behind regression while actively applying it. Working with many case studies, projects, and exercises from areas such as engineering, business, social sciences, and the physical sciences, students discover the purpose of regression and learn how, when, and where regression models work. The book covers the analysis of observational data as well as of data that arise from designed experiments. Special emphasis is given to the difficulties when working with observational data, such as problems arising from multicollinearity and "messy" data situations that violate some of the usual regression assumptions. Throughout the text, students learn regression modeling by solving exercises that emphasize theoretical concepts, by analyzing real data sets, and by working on projects that require them to identify a problem of interest and collect data that are relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models.
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