Applied regression an introduction by Colin lewis beck

By:

Colin lewis beck [Author]

Contributor(s):

Michael Lewis-Beck

Publication details: Sage New Delhi 2016Edition: 2nd EdDescription: xv, 103 pages; Illustrations: 21 cmISBN:

9781483381473

DDC classification:

519.536

Contents:

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

Abstract: 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. 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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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Indian Institute of Management Visakhapatnam General Stacks	Non-fiction	519.536 LEW (Browse shelf(Opens below))	Checked out	04/23/2023	001254

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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 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. 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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