Description

Book Synopsis
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.

Table of Contents
Introduction; Simple Linear Regression; Multiple Linear Regression; Detection of Outliers and Influential Observations in Multiple Linear Regression; Model Selection in Linear Regression; Linear Regression for Models with Heterogeneous Error; Regression Model for Discrete Response Variables; Variable Transformation in Linear Regression.

Linear Regression Analysis: Theory And Computing

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    A Hardback by Xin Yan, Xiaogang Su

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      Publisher: World Scientific Publishing Co Pte Ltd
      Publication Date: Publication Date: 22/06/2009
      ISBN13: 9789812834102, 978-9812834102
      ISBN10: 9812834109

      Description

      Book Synopsis
      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.

      Table of Contents
      Introduction; Simple Linear Regression; Multiple Linear Regression; Detection of Outliers and Influential Observations in Multiple Linear Regression; Model Selection in Linear Regression; Linear Regression for Models with Heterogeneous Error; Regression Model for Discrete Response Variables; Variable Transformation in Linear Regression.

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