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

Linear Regression Analysis: Theory and Computing PDF

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