Introduction to Linear Regression Analysis

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Release : 2015-06-29
Genre : Mathematics
Kind :
Book Rating : 171/5 ( reviews)

Introduction to Linear Regression Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Linear Regression Analysis write by Douglas C. Montgomery. This book was released on 2015-06-29. Introduction to Linear Regression Analysis available in PDF, EPUB and Kindle. Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

Solutions Manual to accompany Introduction to Linear Regression Analysis

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Release : 2013-04-23
Genre : Mathematics
Kind :
Book Rating : 507/5 ( reviews)

Solutions Manual to accompany Introduction to Linear Regression Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Solutions Manual to accompany Introduction to Linear Regression Analysis write by Douglas C. Montgomery. This book was released on 2013-04-23. Solutions Manual to accompany Introduction to Linear Regression Analysis available in PDF, EPUB and Kindle. As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.

Regression Analysis and Linear Models

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Release : 2016-09-27
Genre : Social Science
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Book Rating : 134/5 ( reviews)

Regression Analysis and Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Analysis and Linear Models write by Richard B. Darlington. This book was released on 2016-09-27. Regression Analysis and Linear Models available in PDF, EPUB and Kindle. Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable?s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Linear Regression Analysis

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Author :
Release : 2009
Genre : Mathematics
Kind :
Book Rating : 109/5 ( reviews)

Linear Regression Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Linear Regression Analysis write by Xin Yan. This book was released on 2009. Linear Regression Analysis available in PDF, EPUB and Kindle. "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 techniques described in the book. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject area." --Book Jacket.

Introduction to Regression Analysis

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Author :
Release : 2004
Genre : Mathematics
Kind :
Book Rating : 241/5 ( reviews)

Introduction to Regression Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Regression Analysis write by Michael A. Golberg. This book was released on 2004. Introduction to Regression Analysis available in PDF, EPUB and Kindle. In order to apply regression analysis effectively, it is necessary to understand both the underlying theory and its practical application. This book explores conventional topics as well as recent practical developments, linking theory with application. Intended to continue from where most basic statistics texts end, it is designed primarily for advanced undergraduates, graduate students and researchers in various fields of engineering, chemical and physical sciences, mathematical sciences and statistics.