Description

Book Synopsis

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


Table of Contents
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.

Regression Analysis in R

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    A Paperback by Jocelyn E. Bolin

    £999.99

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      Book details

      Publisher Chapman & Hall/CRC
      Published 7/27/2022 12:00:00 AM
      ISBN-13 9780367272586
      978-0367272586
      ISBN-10 036727258X
      Also in:
      Social research and statistics

      Description

      Book Synopsis

      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


      Table of Contents
      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.

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