Applied Linear Regression Models

by Michael H. Kutner, Christopher J Nachtsheim, John Neter

Applied Linear Regression Models continues to provide a blend of the theory and applications of regression models in real life situations. In this Fourth Edition, the authors have provided improved organization and expanded coverage of key concepts such as model selection and validation, logistic regression, regression trees, and neural networks. The use of graphics and real-world data sets in examples, exercises, and case studies has been intensified, reflecting the ever-increasing role of computing in statistics and modeling.

Genres: Textbooks, Mathematics, Science, Reference

2004 pages, Paperback

First published September 3, 2003

Reviews

[Eric · Rating 5 out of 5]

Very thorough for those who are new to modeling and want to know what goes behind these concepts. I give it five stars as a total beginner. Anyone who isn’t a total beginner will likely find this book frustrating.

[Ken · Rating 5 out of 5]

I have to say, the appendices and the relatively deep level of detailed derivations the authors included pay such huge dividends for a textbook focusing on the theory behind linear regression topics. It's so helpful to students but it means more work for the authors. Not all authors in the business of writing textbooks are willing to do that. So I'm grateful for the effort. It's a great book to keep as a reference.

[Brendan · Rating 1 out of 5]

I really disliked the way this was put together. Lots and lots of needless explanation that goes too far for an applied text and not far enough for a theoretical one. Bloated and unappealing, this should be thrown in the rubbish heap.

At this point, applied information is all over the internet. No need to pick up a bloated text from 20 years ago.

[James · Rating 5 out of 5]

This was my favorite textbook in college. Thanks Dr. Kutner

[May Ling · Rating 4 out of 5]

Among books on this topic, this one is pretty good. For those that may not have obtained the material sequentially, the appendix is recommended. I can see some giving it lower marks, as it does not fully treat the topic in terms of matrices. I however, prefer that. There is a one chapter that does go through the matrix math as a review.

I do wish there was a companion answer key for all of the problems in the book. Also, at times, a few of the problems are a bit contrived.

[Jerzy · Rating 4 out of 5]

Pretty clear - does a really decent job of introducing the concepts of regression and presenting the most important proof concepts without giving too much detail for an applied book. Lots of good problems to work through.

[Wei Xu · Rating 4 out of 5]

This is a very kind of book for applied statistical students who don't cover much math backgrounds, of course, it's also a good instructor to math students if you want to know more about the statistic more systematically.

[Amy Nguyen · Rating 4 out of 5]

Love how they show regression examples to predict