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Solutions Manual to Accompany Introduction to Linear Regression Analysis, Fifth Edition

Ebook Central Academic Complete Available online

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O'Reilly Online Learning: Academic/Public Library Edition Available online

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Format:
Book
Author/Creator:
Montgomery, Douglas C.
Contributor:
Peck, Elizabeth A.
Vining, G Geoffrey.
Ryan, Ann G.
Language:
English
Subjects (All):
Correlation (Statistics).
Mathematics.
Regression analysis.
Physical Description:
1 online resource (105 pages)
Edition:
5th ed.
Place of Publication:
John Wiley & Sons (US)
Somerset : John Wiley & Sons, Incorporated, 2013.
Summary:
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.
Contents:
Intro
Half Title page
Title page
Copyright page
Preface
Chapter 2: Simple Linear Regression
Chapter 3: Multiple Linear Regression
Chapter 4: Model Adequacy Checking
Chapter 5: Transformations and Weighting to Correct Model Inadequacies
Chapter 6: Diagnostics for Leverage and Influence
Chapter 7: Polynomial Regression Models
Chapter 8: Indicator Variables
Chapter 9: Multicollinearity
Chapter 10: Variable Selection and Model Building
Chapter 11: Validation of Regression Models
Chapter 12: Introduction to Nonlinear Regression
Chapter 13: Generalized Linear Models
Chapter 14: Regression Analysis of Time Series Data
Chapter 15: Other Topics in the Use of Regression Analysis.
Notes:
Preface signed by Anne G. Ryan, Dana C. Krueger, Scott M. Kowalski.
Description based on publisher supplied metadata and other sources.
ISBN:
1-118-54850-7

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