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Introduction to Linear Regression Analysis - Student Solution Manual

Introduction to Linear Regression Analysis - Student Solution Manual - 5th edition

ISBN13: 978-1118471463

Cover of Introduction to Linear Regression Analysis - Student Solution Manual 5TH 13 (ISBN 978-1118471463)
ISBN13: 978-1118471463
ISBN10: 1118471466
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Edition/Copyright: 5TH 13
Publisher: John Wiley & Sons, Inc.
Published: 2013
International: No

List price: $31.95

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Introduction to Linear Regression Analysis - Student Solution Manual - 5TH 13 edition

ISBN13: 978-1118471463

Douglas C. Montgomery

ISBN13: 978-1118471463
ISBN10: 1118471466
Cover type:
Edition/Copyright: 5TH 13
Publisher: John Wiley & Sons, Inc.

Published: 2013
International: No
Summary

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, allowin

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