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Applied regression : an introduction Colin, Lewis-Beck, Michael S. Lewis-Beck

By: Contributor(s): Material type: TextTextSeries: Quantitative applications in the social sciences ; Vol. no. 07-022Publication details: Los Angeles : Sage, c2016.Edition: 2nd edDescription: xvi, 103 p. : ill ; 21 cmISBN:
  • 9781483381473
Subject(s): DDC classification:
  • 519.5/36
Online resources: Summary: Known for its readability and clarity, this Second Edition 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. The authors 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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Holdings
Item type Current library Call number Status Date due Barcode
REGULAR University of Wollongong in Dubai Main Collection 519.536 LE AP (Browse shelf(Opens below)) Available T0051882

Includes bibliographical references and index.

Known for its readability and clarity, this Second Edition 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. The authors 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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