Acknowledgements; Introduction; Chapter 1. What is statistics?: Main statistical notions and principles; Chapter 2. Introduction to R; Chapter 3. Descriptive statistics for quantitative variables; Chapter 4. How to explore qualitative variables: proportions and their visualizations; Chapter 5. Comparing two groups: t-test and Wilcoxon and Mann-Whitney tests for independent and dependent samples; Chapter 6. Relationships between two quantitative variables: Correlation analysis with elements of linear regression modelling; Chapter 7. More on frequencies and reaction times: Linear regression; Chapter 8. Finding differences between several groups: Sign language, linguistic relativity and ANOVA; Chapter 9. Measuring associations between two categorical variables: Conceptual metaphors and tests of independence; Chapter 10. Association measures: collocations and collostructions; Chapter 11. Geographic variation of quite: Distinctive collexeme analysis; Chapter 12. Probabilistic multifactorial grammar and lexicology: Binomial logistic regression; Chapter 13. Multinomial (polytomous) logistic regression models of three and more near synonyms; Chapter 14. Conditional inference trees and random forests; Chapter 15. Behavioural profiles, distance metrics and cluster analysis; Chapter 16. Introduction to Semantic Vector Spaces: Cosine as a measure of semantic similarity; Chapter 17. Language and space: Dialects, maps and Multidimensional Scaling; Chapter 18. Multidimensional analysis of register variation: Principal Components Analysis and Factor Analysis; Chapter 19. Exemplars, categories, prototypes: Simple and multiple correspondence analysis; Chapter 20. Constructional change and motion charts; Epilogue; The most important R objects and basic operations with them; Main plotting functions and graphical parameters in R; References;
