Regression Analysis in R

A Comprehensive View for the Social Sciences

AvJocelyn E. Bolin

Inbunden, Engelska, 2022

Del i serien Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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Beskrivning

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 interpretationFull syntax examples to help teach R codeAppendix explaining basic R functionsMethods for multilevel data that are often included in basic regression textsEnd of Chapter Comprehension Exercises

Produktinformation

Utgivningsdatum:2022-07-27
Mått:156 x 234 x 15 mm
Vikt:421 g
Format:Inbunden
Språk:Engelska
Serie:Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Antal sidor:180
Förlag:Taylor & Francis Ltd
ISBN:9781032257754

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Mer om författaren

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.

Innehållsförteckning

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.