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A Modern Approach to Regression with R (Springer Texts in Statistics)

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A Modern Approach to Regression with R (Springer Texts in Statistics) Cover

 

Synopses & Reviews

Publisher Comments:

A Modern Approach to Regression with R focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. The regression output and plots that appear throughout the book have been generated using R. On the book website you will find the R code used in each example in the text. You will also find SAS-code and STATA-code to produce the equivalent output on the book website. Primers containing expanded explanations of R, SAS and STATA and their use in this book are also available on the book website. The book contains a number of new real data sets from applications ranging from rating restaurants, rating wines, predicting newspaper circulation and magazine revenue, comparing the performance of NFL kickers, and comparing finalists in the Miss America pageant across states. One of the aspects of the book that sets it apart from many other regression books is that complete details are provided for each example. The book is aimed at first year graduate students in statistics and could also be used for a senior undergraduate class. Simon Sheather is Professor and Head of the Department of Statistics at Texas A&M University. Professor Sheather's research interests are in the fields of flexible regression methods and nonparametric and robust statistics. He is a Fellow of the American Statistical Association and listed on ISIHighlyCited.com.

Synopsis:

This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on valid models.

Table of Contents

Introduction.- Simple linear regression.- Diagnostics and transformations for simple linear regression.- Weighted least squares.- Diagnostics and transformations for multiple linear regression.- Variable selection.- Logistic regression.- Serially correlated errors.- Mixed models.- Appendix: Nonparametric smoothing.

Product Details

ISBN:
9781441918727
Author:
Sheather, Simon
Publisher:
Springer
Location:
New York, NY
Subject:
Statistics
Subject:
correlated errors
Subject:
mixed models
Subject:
Regression analysis
Subject:
regression diagnostics
Subject:
regression modeling strategies
Subject:
Statistical Theory and Methods
Subject:
Econometrics <P>Compares a number of new real data sets that enable students to learn how regression can be used in real life</P> <P>Provides R code used in each example in the text along with the SAS-code and STATA-code to produce the equivalent output</
Subject:
Economics - General
Subject:
Econometrics
Subject:
The Arts
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2009
Series:
Springer Texts in Statistics
Publication Date:
20101129
Binding:
TRADE PAPER
Language:
English
Pages:
407
Dimensions:
235 x 155 mm 614 gr

Related Subjects

History and Social Science » Economics » General
Science and Mathematics » Biology » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

A Modern Approach to Regression with R (Springer Texts in Statistics) New Trade Paper
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Product details 407 pages Springer - English 9781441918727 Reviews:
"Synopsis" by , This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on valid models.
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