Statistics for Linguists: An Introduction Using R
eBook - ePub

Statistics for Linguists: An Introduction Using R

  1. 310 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Statistics for Linguists: An Introduction Using R

About this book

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.

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Yes, you can access Statistics for Linguists: An Introduction Using R by Bodo Winter in PDF and/or ePUB format, as well as other popular books in Education & Teaching Arts & Humanities. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Routledge
Year
2019
Print ISBN
9781138056084
eBook ISBN
9781351677424
Edition
1
Topic
Education
Subtopic
Teaching Arts & Humanities

Table of contents

  1. Cover
  2. Half Title
  3. Title
  4. Copyright
  5. Contents
  6. Acknowledgments
  7. 0. Preface: Approach and How to Use This Book
  8. 1 Introduction to R
  9. 2 The Tidyverse and Reproducible R Workflows
  10. 3 Descriptive Statistics, Models, and Distributions
  11. 4 Introduction to the Linear Model: Simple Linear Regression
  12. 5 Correlation, Linear, and Nonlinear Transformations
  13. 6 Multiple Regression
  14. 7 Categorical Predictors
  15. 8 Interactions and Nonlinear Effects
  16. 9 Inferential Statistics 1: Significance Testing
  17. 10 Inferential Statistics 2: Issues in Significance Testing
  18. 11 Inferential Statistics 3: Significance Testing in a Regression Context
  19. 12 Generalized Linear Models 1: Logistic Regression
  20. 13 Generalized Linear Models 2: Poisson Regression
  21. 14 Mixed Models 1: Conceptual Introduction
  22. 15 Mixed Models 2: Extended Example, Significance Testing, Convergence Issues
  23. 16 Outlook and Strategies for Model Building
  24. References
  25. Appendix A. Correspondences Between Significance Tests and Linear Models
  26. Appendix B. Reading Recommendations
  27. Index
  28. Index of R Functions