Description:

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

349 Pages·2009·2.722 MB·English

by Xin Yan, Xiao Gang Su

#Mathematics #Applied Mathematicsematics

Checking for file health...

Download

Upgrade Premium

Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.

Linear Regression Analysis: Theory and Computing • Digital Edition • Free Download

By Xin Yan, Xiao Gang Su| File: 2.722| English| 2009

#Mathematics #Applied Mathematicsematics

The digital edition of "Linear Regression Analysis: Theory and Computing" is available from our community library collection.

Retrieve Document

Resource Overview

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

Publication Details

Title:	Linear Regression Analysis: Theory and Computing
Author:	Xin Yan, Xiao Gang Su
Published by:
Publication date:	2009
ISBN:	9789812834102
Document length:	349 pages
Written in:	English
Digital size:	2.722
Access type:	Library Collection • No Cost

Reader Resources

Digital Reading Tips

This document works on all major e-readers and devices
Adjust brightness for comfortable extended reading
Use bookmarking features to track your progress
Searchable text helps locate specific content quickly

Library Collection Policy

Zlibrary.cc maintains an extensive collection of digital documents for educational and research purposes. We believe in providing access to knowledge for all.

Access "Linear Regression Analysis: Theory and Computing" Document

Available in multiple formats • No account creation required

Common Questions

Is this the complete version of "Linear Regression Analysis: Theory and Computing"?

Yes, this is the complete digital edition of "Linear Regression Analysis: Theory and Computing" by Xin Yan, Xiao Gang Su. The document includes all content from the original publication with no omissions.

What is Zlibrary.cc's approach to digital resources?

Zlibrary.cc serves as a catalog and access point to educational materials freely available across the internet. We do not host these files on our servers but provide links to sources where they can be accessed. Our mission is to support education and research by making knowledge more discoverable. Users should adhere to copyright laws in their jurisdiction when accessing these resources.