Applied Regression

An Introduction

Nonfiction, Reference & Language, Reference, Research, Social & Cultural Studies, Social Science
Cover of the book Applied Regression by Colin Lewis-Beck, Michael S. Lewis-Beck, SAGE Publications
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Author: Colin Lewis-Beck, Michael S. Lewis-Beck ISBN: 9781483381497
Publisher: SAGE Publications Publication: June 18, 2015
Imprint: SAGE Publications, Inc Language: English
Author: Colin Lewis-Beck, Michael S. Lewis-Beck
ISBN: 9781483381497
Publisher: SAGE Publications
Publication: June 18, 2015
Imprint: SAGE Publications, Inc
Language: English

Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. Authors Colin Lewis-Beck and Michael Lewis-Beck then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.

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Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. Authors Colin Lewis-Beck and Michael Lewis-Beck then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.

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