Solutions Manual to Accompany Introduction to Linear Regression Analysis

by Ann G. Ryan, Douglas C. Montgomery, Elizabeth A. Peck

Wiley | March 31, 2014 | Kobo Edition (eBook)

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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.

Format: Kobo Edition (eBook)

Published: March 31, 2014

Publisher: Wiley

Language: English

The following ISBNs are associated with this title:

ISBN - 10: 1118548507

ISBN - 13: 9781118548509

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Solutions Manual to Accompany Introduction to Linear Regression Analysis

by Ann G. Ryan, Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

Format: Kobo Edition (eBook)

Published: March 31, 2014

Publisher: Wiley

Language: English

The following ISBNs are associated with this title:

ISBN - 10: 1118548507

ISBN - 13: 9781118548509

From the Publisher

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.
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