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Linear Regression Analysis: Theory and Computing

Xin Yan and Xiao Gang Su
World Scientific
Publication Date: 
Number of Pages: 
[Reviewed by
Ita Cirovic Donev
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Linear Regression Analysis provides yet another graduate level text on regression analysis. So what distinguishes this book from the now quite large pile of books on linear regression analysis? Being just a passive reader (I didn’t study from the book), I can observe that the author paid a bit more attention to details when it comes to proofs, i.e. certain middle steps are presented. The addition of a chapter on LASSO and ridge regression, which are not discussed in many books of the same title, is another distinctive point.

The book is written in a narrative style with mathematical exposition where it deems necessary. Even though the authors place the book at the graduate level, the book does not, in any sense, follow a strict theoretical presentation of the concepts. One could say that it is rather “user friendly.” The authors include illustrative examples, using SAS as a statistical application of choice. The SAS code is presented as well, but the output from the SAS is rewritten to follow the presentation style of the book. Naturally, the examples are applied, using data to illustrate the concepts and discussion. Discussions and explanation of the examples and steps used are thorough and clear.

Including so much detail has allowed the number of errors to increase as well. An errata sheet would be a welcome companion when reading the text. Apart from these glitches, the book could be a useful guide for linear regression analysis for students and practitioners.

Ita Cirovic Donev holds a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical methods for credit and market risk. Apart from the academic work she does statistical consulting work for financial institutions in the area of risk management.

  • Introduction
  • Simple Linear Regression
  • Multiple Linear Regression
  • Detection of Outliers and Influential Observations in Multiple Linear Regression
  • Model Selection
  • Model Diagnostics
  • Extensions of Least Squares
  • Generalized Linear Models
  • Bayesian Linear Regression