Introduction to Linear Regression Analysis, 3rd Edition

Rating:
Summary: Excellent introduction to linear regression
Review: 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).


Rating:
Summary: An excellent book
Review: This is one of the best books on the market for a course on linear regression analysis. It contains everything from the basic least squares equations to problems of multicollinearity, and a section on non-linear regression is also thrown in for good measure. Not a cheap book (Wiley would do well to consider inexpensive paperback editions for non-plutocrats)but highly recommended nevertheless. The book contains many applications to real-life problems and questions are included at the ends of the chapters, making this a valuable textbook for a very wide range of students.