Customer Reviews

Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics)

5 star:		(4)
4 star:		(2)
3 star:		(0)
2 star:		(2)
1 star:		(1)

 

 

If you have a desire or need to develop regression models, whether for prediction or classification, this is a great place to start climbing the learning curve. The book covers all the essentials, such as how to fit a model to a set of data, how to evaluate the quality of the fit, and how to detect influential data points. It also does a good job with some of the issues involved in fitting a regression (most notably colinearity, overfitting, outliers, and deviations from normality) and discusses ridge regression, principal components regression, and other so-called "robust" methods for dealing with such issues. Even if you plan to use nonlinear modelling techniques like polynomial regression or feed-forward neural networks, this book is worth reading: many of the same issues that are involved when developing linear regression models arise in the context of nonlinear models. I use multivariate polynomial regression models for pricing options, and cite this book in my own recent work on that subject--"Advanced Option Pricing Models" (McGraw Hill, Feb 2005).

Jeffrey Owen Katz, Ph.D.
Author (with Donna L. McCormick) of "The Encyclopedia of Trading Strategies" (McGraw Hill, 2000).

I have access to this, the third edition and the latest, the fourth edition, through my company's library. There is really no material difference in the content and I was able to save about 80% of the purchase price by buying a used copy of the third edition, vs. new copy of fourth edition.

Wonderful book for self study. You will benefit most if you have a good background in probability theory and linear algebra and want to understand the details and language of linear regression. Even without that background chapters one through three will teach you more than you will ever learn in most survey courses in statistics. To fully appreciate the whole book I think you need a one semester course in linear algebra and one or two semesters of probability theory.

This was used as the textbook in a course in Linear Regression
Analysis that I recently attended as an auditor. I'm a mathematician,
not a statistician, so much of the material, and the authors' ways of
looking at it, were not familiar to me. My statistician colleagues
assure me that the techniques in the book are correct, useful, and
mostly up to date. And I believe them.

Alas, the book is poorly edited, and in just the few chapters we
covered, I found a score of errors, including misstated formulas,
misplaced graphics, multiplication where there should be division, and
even some numerical errors. If you, as an instructor, decide to adopt
this book for your course, be prepared to do a lot of proofreading
(the publisher apparently didn't bother) and to distribute textbook
corrections to your students. Also note that this book, like so many
Wiley textbooks, is overpriced.

This is a good book with good exercises in the end of the chapters, but a little hard to read.

very useful for industrial applications. There are quite a few printing mistakes and that would be a problem for those reader they are not very strong in statistics.

Firstly, I am not stats guru, a mere mortal trying to get through a top-10 MBA program. I picked this book (and the companion text) to extend my knowledge, and it is helping. Tough for the gifted amateur, but interesting and the depth of the analysis truly helps.

Major negative comment : The manual often provides one sentence answers to mathematical questions. For example : "....slight improvement in the model." But this is not helpful. It lacks truly worked answers to the questions; which is essential to help those who are struggling to get to the right level.

This is not a limited observation, it is the trend and considerably more work could be done to explain how answers were derived, as it also helps find out why an answer might not be correct.

As for the up side. All chapter questions are answered and the FTP site provides raw data files. This is greatly appreciated. In balance, the weaknesses are significant.

The book was in great condition when received and was significantly cheaper at Amazon(even brand new) than anywhere else I had looked. Even though it focuses on engineering examples, it does a good job of explaining the subject matter in a logical format accompanied with visual examples to further illustrate the concepts. If you use regression at all, this seems like a great edition to your bookshelf. Also, it is available as a hardcover, so it will stand the test of time better than a paperback.

I blamed myself for this wrong purchase, and hope you can avoid it. This is a book of solution for questions asked in Linear Regression Analysis, not the book. This is a collection of answers.

it saids the book is used but like new. However, the book is actually totally new, never used before,no pollution at all. The price is also one of the lowest compare with others. Excellent!!