Linear Regression Analysis: Theory and Computing Review

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(More customer reviews)This book is quite concise, but there are many errors or oversight. For instance, terms introduced in proofs were not given or discussed earlier, making it difficult to follow the proof. Another example is just sheer sloppiness, such as on p.45, where the last line of the first paragraph is clearly incorrect. On the same page, another elementary error appears in the definition of orthonormal basis (orthognality is missing). Readers will have little confidence in the proofs or formulas.

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This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields.

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

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