Applied regression : an introduction
By: Lewis-Beck, Colin
Title By: Lewis-Beck, Michael S
Material type:
BookSeries: Quantitative applications in the social sciences ; Vol. no. 07-022.Publisher: Los Angeles : Sage, c2016.Edition: 2nd ed.Description: xvi, 103 p. : ill ; 21 cm.ISBN: 9781483381473Subject(s): Regression analysisDDC classification: 519.5/36 Online resources: Location Map | Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
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| REGULAR | University of Wollongong in Dubai Main Collection | 519.536 LE AP (Browse shelf) | Available | T0051882 |
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| 519.536 AB IN Introduction to regression modeling | 519.536 AB IN Introduction to regression modeling | 519.536 KU AP Applied linear statistical models | 519.536 LE AP Applied regression : | 519.536 LO RE Regression models for categorical dependent variables using Stata / | 519.536 LO RE Regression models for categorical dependent variables using Stata / | 519.538 FR FO Foundations of statistics / |
Includes bibliographical references and index.
Series Editor's Introduction Preface Acknowledgments About the Authors 1. Bivariate Regression: Fitting a Straight Line 2. Bivariate Regression: Assumptions and Inferences 3. Multiple Regression: The Basics 4. Multiple Regression: Special Topics Appendix References 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.