An edition of Regression & Linear Modeling (2016)

Regression & Linear Modeling

Best Practices and Modern Methods

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An edition of Regression & Linear Modeling (2016)

Regression & Linear Modeling

Best Practices and Modern Methods

by

  • 0 Ratings
  • 4 Want to read
  • 0 Currently reading
  • 0 Have read

This book provides conceptual, user-friendly coverage of the generalized linear model (GLM). Themes covered include testing assumptions, examining data quality, and nonlinear and non-additive effects modelled within different types of linear models.

Publish Date
Publisher
SAGE Publications, Inc
Language
English
Pages
486

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Subjects
Regression analysis, Linear models, Analysis of variance, Mathematical statistics, Statistical methods, Linear models (statistics), Linear models (Statistics)

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Edition Availability
Cover of: Regression & Linear Modeling
Regression & Linear Modeling: Best Practices and Modern Methods
2017, SAGE Publications, Inc
Hardcover in English
  • Not in Library
Libraries near you: WorldCat
Cover of: Regression and Linear Modeling
Regression and Linear Modeling: Best Practices and Modern Methods
2016, SAGE Publications, Incorporated
in English
Libraries near you: WorldCat
Cover of: Regression & Linear Modeling
Regression & Linear Modeling: Best Practices and Modern Methods
May 5, 2016, SAGE Publications, Inc
Hardcover in English
Libraries near you: WorldCat

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Book Details

Table of Contents

-A Nerdly manifesto --
-Basic estimation and assumptions --
-Simple linear models with continuous dependent variables : simple regression analyses --
-Simple linear models with continuous dependent variables : simple ANOVA analyses ---
-Simple linear models with catagorical dependent variables : multinomial and ordinal logistic -regression --
-Simple curvilinear models --
-Multiple independent variables --
-Interactions between independent variables : simple moderation --
-Curvilinear interactions between independent variables --
-Poisson models : low-frequency count data as dependent variables --
-Long-linear models : general linear models when all of your variables are unordered categorical --
-A brief introduction to hierarchical linear modeling --
-Missing data in linear modeling --
-Trustworthy science : improving statistical reporting --
-Reliable measurement matters --
-Prediction in the generalized linear model --
-Modeling in large, complex samples : the importance of using appropriate weights and design effect compensation.

Edition Notes

Includes bibliographical references and index.

Published in
Los Angeles, USA
Other Titles
Regression And Linear Modeling
Copyright Date
©2017

Classifications

Dewey Decimal Class
519.5/36
Library of Congress
QA278.2 .O833 2017, QA278.2.O833 2017

The Physical Object

Format
Hardcover
Pagination
xxix, 457 pages : illustrations ; 26 cm
Number of pages
486
Dimensions
7 x 1 x 10 inches
Weight
2 pounds

ID Numbers

Open Library
OL26857253M
ISBN 10
1506302769
ISBN 13
9781506302768
LCCN
2015042929
OCLC/WorldCat
922911476

Source records

Better World Books record
Library of Congress MARC record
ISBNdb

Work Description

In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

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History

Download catalog record: RDF / JSON
September 21, 2020 Edited by MARC Bot import existing book
August 5, 2020 Edited by ImportBot import existing book
April 21, 2019 Edited by Kaustubh Chakraborty Added new cover
April 21, 2019 Edited by Kaustubh Chakraborty Added second edition
April 21, 2019 Created by Kaustubh Chakraborty Added new book.