Linguistic Data: Quantitative Analysis and Visualisation for theoretical linguists

Материалы по математике, 2018-19 учебный год
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Course info

Dear students,

Here will be published the materials of the course "Linguistic Data: Quantitative Analysis and Visualisation", taught at the Master programme "Linguistic Theory and Language Description" in 2018-2019 academic year.

  • Instructors: Olga Lyashevskaya, George Moroz, Alla Tambovtseva and Ilya Schurov.
  • Modules: 3-4

Software

During this course we will use R as a programming language and RStudio as a GUI.

How to install R and RStudio?

1. Download R (you can choose another mirror here if you wish) and install it on your computer. Make sure you did it before installing RStudio.

2. Download RStudio (you need RStudio Desktop Open Source License) and install it on your computer. It is recommended to create a shortcut for RStudio during installation.

It is possible avoid installing anything on your PC, using online version of RStudio.

How to use RStudio?

Read the instruction here.

For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd).

Materials

Date Topic of the lecture Seminar Optional
12.01 Something about data: population vs sample

Descriptive statistics

problems1 R-basics RMarkdown: official page,

cheatsheet

19.01 Population and samples. Working with data in R problems2 R-samples artists.txt

R-vectors R-dataframes orientation.csv

more on basic graphs in R
26.01 Statistical hypotheses testing Binomial-test poetry.csv
02.02 Student's t-test. Central limit theorem T-test icelandic.csv asp-paper (Coretta, 2017)
09.02 Confidence Intervals Conf-intervals poetry.csv icelandic.csv an interactive visualization of CI by K.Magnusson

more on overlapping CI's (by A.Knezevic)

16.02 Data manipulation with tidyverse. Visualisation with ggplot2 class materials
02.03 Chi-squared and Fisher's exact tests Chi-squared-test elision.csv socling.csv
16.03 Correlation coefficients and simple linear regression Corr-regressioneducation.csvchekhov.csv guess correlation game
23.03 Multiple comparisons. ANOVA Anova icelandic.csv correlograms spurious correlations
06.04 Multiple linear regression Multiple-regression english.csv more on visualising coefficients, more tests
13.04 Logistic regression Lab10 [Lab10-solutions] more on visualising coefficients
27.04 More on model diagnostics. Mixed-effects models Mixed-effects ReductionRussian.txt LME in R
18.05 Decision trees and random forest. Lab 12. Trees and forests Code
25.05 PCA class materials

R seminars in pdf

12 January: R-basics, 19 January: R-vectors, R-dataframes, R-samples, 26 January: Binomial-test

2 February: T-test, 9 February: Conf-intervals

02 March: Chi-squared-test, 16 March: Corr-regression, 23 March: Anova

6 April: Multiple-regression, 27 April: Mixed-effects

R seminars in .R and .Rmd

12 January: R-basics.R, R-basics.Rmd, 19 January: R-vectors.R, R-vectors.Rmd R-dataframes.R, R-dataframes.Rmd, R-samples.Rmd, 26 January: Binomial-test.Rmd

2 February: T-test.R, T-test.Rmd, 9 February: Conf-intervals.Rmd Conf-intervals.R

2 March: Chi-squared-test.Rmd, Chi-squared-test.R, 16 March: Corr-regression.R, Corr-regression.Rmd, 23 March: Anova.R, Anova.Rmd

6 April: [Multiple-regression.R], [Multiple-regression.Rmd], 27 April: Mixed-models.R, Mixed-models.Rmd

Homeworks

Final project

  • Projects description
  • Project topics: link to the table to fill in
  • Projects pre-registration (deadline: 28 April, 23:59): link to submit your file