Applied Regression
- 0 %
Der Artikel wird am Ende des Bestellprozesses zum Download zur Verfügung gestellt.

Applied Regression

An Introduction
 EPUB
Sofort lieferbar | Lieferzeit: Sofort lieferbar I

Unser bisheriger Preis:ORGPRICE: 27,99 €

Jetzt 27,98 €*

ISBN-13:
9781483381497
Einband:
EPUB
Seiten:
120
Autor:
Colin Lewis-Beck
Serie:
Quantitative Applications in the Social Sciences
eBook Typ:
Adobe Digital Editions
eBook Format:
EPUB
Kopierschutz:
Adobe DRM [Hard-DRM]
Sprache:
Englisch
Beschreibung:

Updates to this new edition include: more coverage of regression assumptions and model fit; additional material on residual analysis; more examples of transformations; and the inclusion of the measures of tolerance and VIF within the discussion about collinearity. 

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.




Available with
 
Perusall
—an eBook that makes it easier to prepare for class


Perusall
 
is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. 
Learn more
.
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