Welcome to the companion site for the course *LFIAL2260 - Statistics for linguistics*. All materials on this website are released under the terms of the CC BY-NC-SA 4.0 license.

The slides for the classes are available for download here:

- Topic 1: Introduction: Statistics for linguistics, R, Rstudio, and using R as a calculator
- Topic 2: Distributions and basic descriptive statistics for quantitative variables
- Topic 3: Exploring qualitative variables
- Topic 4: Comparing the central tendency of two groups
- Topic 5: Correlations
- Topic 6: Associations between qualitative variables
- Topic 7: Linear regression: Theory and assumptions
- Topic 8: The science (or art?) of fitting linear regression models
- Topic 9: Analysis of Variance
- Topic 10: Logistic regression
- Topic 11: Mixed-effects regression
- Topic 12: When things aren’t quite linear: Polynomial regression and elements of Generalized Additive Modeling

A version of the slides rendered as Word documents can be found here so can you take notes more easily:

- Topic 2: Distributions and basic descriptive statistics for quantitative variables
- Topic 3: Exploring qualitative variables
- Topic 4: Comparing the central tendency of two groups
- Topic 5: Correlations
- Topic 6: Associations between qualitative variables
- Topic 7: Linear regression: Theory and assumptions
- Topic 8: The science (or art?) of fitting linear regression models
- Topic 9: Analysis of Variance
- Topic 10: Logistic regression
- Topic 11: Mixed-effects regression
- Topic 12: When things aren’t quite linear: Polynomial regression and elements of Generalized Additive Modeling

- Lab 1: Objects, vectors, and basic mathematics
- Lab 2: Data frames, measures of central tendency, measures of dispersion, and distributions
- Lab 3: Data frames (recap), normal distribution and outliers (recap), and exploring qualitative variables
- Lab 4: Comparing the central tendency of two groups
- Lab 5: Correlations
- Lab 6: Associations between qualitative variables
- Lab 7: Linear regression: Theory and assumptions
- Lab 8: The science (or art?) of fitting linear regression models
- Lab 9: Analysis of Variance
- Lab 10: Logistic regression
- Lab 11: Mixed-effects regression
- Lab 12: When things aren’t quite linear: Polynomial regression and elements of Generalized Additive Modeling

- Baayen, R. H. (2008).
*Analyzing Linguistic Data: A Practical Introduction to Statistics Using R.*Cambridge: Cambridge University Press. - Burnham, K., & Anderson, D. (2002).
*Model selection and multi-model inference: A practical information-theoretic approach*. New York, NY: Springer. - Field, A., Miles, J., & Field, Z. (2012).
*Discovering statistics using R*. New York, NY/London: SAGE. - Fox, J. & Weisberg, S. (2011).
*An R companion to applied regression*. Los Angeles, LA: Sage. - Hosmer, D., Lemeshow, S., & Sturdivant, R. (2013).
*Applied logistic regression*. Oxford: Wiley. - Gries, S. Th. (2009).
*Quantitative corpus linguistics with R.*Berlin/Boston, MA: De Gruyter.

- Gries, S. Th. (2013).
*Statistics for linguistics with R.*Berlin/Boston, MA: De Gruyter.

- Harrell, F. E. (2015).
*Regression modeling strategies: With applications to linear models, logistic and ordinal Regression, and survival analysis*. New York, NY: Springer. - Levshina, N. (2015).
*How to do linguistics with R.*Amsterdam/Philadelphia, PA: John Benjamins.

- Urdan, T. C. (2010).
*Statistics in plain English.*New York, NY: Routledge.

- Zuur, A. F., Leno, E. L., Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems.
*Methods in Ecology and Evolution*1, 3-14.