Regression Analysis in R
A Comprehensive View for the Social Sciences
1st Edition
Published on 27. July 2022
192 pages
978-1-000-62024-5 (ISBN)
Description
Regression Analysis in R: A Comprehensive View for the Social Sciences covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social science disciplines this book walks readers through bivariate correlation giving them a solid framework from which to expand into more complicated regression models. Concepts are demonstrated using R software and real data examples.
Key Features:
Full output examples complete with interpretation
Full syntax examples to help teach R code
Appendix explaining basic R functions
Methods for multilevel data that are often included in basic regression texts
End of Chapter Comprehension Exercises
Key Features:
Full output examples complete with interpretation
Full syntax examples to help teach R code
Appendix explaining basic R functions
Methods for multilevel data that are often included in basic regression texts
End of Chapter Comprehension Exercises
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
4 Tables, black and white; 22 Line drawings, black and white; 8 Halftones, black and white; 30 Illustrations, black and white
ISBN-13
978-1-000-62024-5 (9781000620245)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
07/2022
1st Edition
Chapman & Hall/CRC
€95.30
Shipment within 15-20 days
Book
07/2022
1st Edition
Chapman & Hall/CRC
€231.40
Shipment within 10-20 days
Person
Jocelyn E. Bolin is professor in the Department of Educational Psychology at Ball State University, where she teaches courses on introductory and intermediate statistics, multiple regression analysis, and multilevel modeling to graduate students in social science disciplines. She earned a PhD in educational psychology from Indiana University Bloomington. Her research interests include statistical methods for classification and clustering and use of multilevel modeling in the social sciences.
Content
1. The Issue of Causality. 2. Describing Simple Relationships. 3. Linear Regression Analysis. 4. Regression Assumptions and Interpretational Considerations. 5. Dummy Variables and Interactions. 6. Hierarchical Regression. 7. Moderation and Mediation. 8. Dealing with Non- Linearity. 9. Regression Models for Nested Data. 10. Fixed Effects Modeling.
