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'''Дорогие третьекурсники!'''
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* Instructors: Ilya Schurov and Olga Lyashevskaya
  
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На этой странице будут появляться различные материалы и объявления, связанные с курсом '''«Математические модели политэкономии»''', читаемого для студентов 3-го курса ОП Политологии в '''2019/2020''' учебном году.
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== Materials ==
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Для специализациии «Политический анализ» настоящая дисциплина является обязательной.
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{| class="wikitable"
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|-
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* Авторы курса: К.И. Сонин, Д.А. Дагаев, Л.Н. Сысоева
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! Data !! Topics !! Links
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* Лекции читает: Сысоева Любовь Николаевна (lsysoeva@hse.ru)
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|-
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* Семинары ведет: Сысоева Любовь Николаевна
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| Jan 18 || Introduction. Quantitative linguistic research and data types. R basics || [https://docs.google.com/presentation/d/1VUIUa3Db5n4dsD_HeA3e-mz55zK8uPrko3yu207pKUk/edit?usp=sharing Intro Slides] [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-01-18 Lab 01: intro to R]
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* Ассистент: Паршина Анастасия (a.a.parshina@ya.ru)
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|-
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| Jan 25 || Hypothesis testing. Binomial test. R: dataframes, tydyverse || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-01-25 Lab 02] [https://datacamp-community-prod.s3.amazonaws.com/e63a8f6b-2aa3-4006-89e0-badc294b179c tidyverse cheat sheet]
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== Материалы ==
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|-
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| Feb 1 || Central limit theorem. Variance. Student's t-test. R: simulating data, boxplots, density plots, binomial test, t-test ||
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=== Электронная ведомость ===
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[https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-01 Lab 03: ]
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https://docs.google.com/spreadsheets/d/1BZ4EQ1ZCpDEzOI-GwDDW-RLdp6_Sfw6qh6jpuUGZetY/edit?usp=sharing
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[https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-01/Lab3-ttest-binom-matrices.Rmd Rmd] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/seminars/2020-02-01/Lab3-ttest-binom-matrices.html html] [https://rforpublichealth.blogspot.com/2014/02/ggplot2-cheatsheet-for-visualizing.html Viz. distributions]
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|-
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=== Лекции ===
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| Feb 8 || Two-sample t-test. Paired t-test. Confidence intervals. Non-parametric tests || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-08 Lab 04: ] [https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-08/Lab4-confint-pairedttest-anova.Rmd Rmd] [https://github.com/LingData2019/LingData2020/raw/master/seminars/2020-02-08/Lab4-confint-pairedttest-anova.pdf pdf][https://agricolamz.github.io/2018-MAG_R_course/Lec_4_stats.html CI slides] [https://istats.shinyapps.io/ExploreCoverage/ CI demo]
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{|class='wikitable'
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|-
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!дата лекции
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| Feb 15 || ANOVA. Correlations || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-15 Lab 05:] [Rmd] [pdf]
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!тема лекции
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|-
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!литература
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| Feb 22 || Tests for categorial data. Chi-squared test. Fisher exact test. Effect size || [https://lindeloev.github.io/tests-as-linear/linear_tests_cheat_sheet.pdf Common statistical tests & linear models ]
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|-
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| Feb 29 || Linear regression. Multivariate linear regression. Dummy variables ||
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|-
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|  || Dimensionality reduction. PCA. MDS. t-SNE ||
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|-
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|  || CA, MCA. Clusterization ||
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|-
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|  || Logistic regression. Model selection ||
 
|-
 
|-
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|17.01
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| || Fixed and random effects. Linear mixed-effects models ||  
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|[https://docviewer.yandex.ru/view/0/?*=513gH0X4nkqZPwLUPPZhvGFnAdt7InVybCI6InlhLWRpc2stcHVibGljOi8vZUNPZGxuNUlGR1U1Nlc3czcrbzFaUmtNbzU5Y1NSZlVTMFNBZFJ1U2J4NGI4S3l5bjhiNEFBUlo5M0JKQ1Iza3EvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoiTGVjdHVyZV8xLnBkZiIsIm5vaWZyYW1lIjpmYWxzZSwidWlkIjoiMCIsInRzIjoxNTc5MzQ3MjE3MjQ0LCJ5dSI6IjQxMjgwODk0MTE1NzkzNDcwNTMifQ%3D%3D Стратегическое финансирование избирательных компаний]
 
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|S. Gehlbach. Formal Models of Domestic Politics. Paragraph 3.1.
 
 
|-
 
|-
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|24.01
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| || Bootstrap. Decision trees. Decision forests ||  
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|[https://docviewer.yandex.ru/view/22496587/?*=5%2F22%2BJzxT1bTO5Ogqbz8DQuIgl17InVybCI6InlhLWRpc2stcHVibGljOi8vMzZLK3N4ZzM3bzJHNC9kM2EvaWNxK2NzWW9pRWtJWVl1eWdNY1Vld0pxakpTT0cwM3dFN3BBS2gzRkZkSTZPN3EvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoiTGVjdHVyZV8yLnBkZiIsIm5vaWZyYW1lIjpmYWxzZSwidWlkIjoiMjI0OTY1ODciLCJ0cyI6MTU3OTg3MzM2ODc4MSwieXUiOiIzOTAwNDcxNjExNTc2MjQwMDA2In0%3D Двухмерная модель Даунса]
 
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|J. Duggan. Formal Models in Political Science. Lecture notes. Lectures 6,7.
 
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S. Gehlbach. Formal Models in Political Science. Paragraph 1.1: The Hotelling-Downs model.
 
 
|-
 
|-
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|24.01
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| || Bayesian statistics ||  
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|[https://docviewer.yandex.ru/view/22496587/?*=Pq8HHP2gq9j9w%2BYISPVwbs%2F8%2F397InVybCI6InlhLWRpc2stcHVibGljOi8vR1VBbGtEZ0pxUVZDTjJuTnlQeElTMm1QWDZScGpXSXlNcHZ5bi85dnFhK3RXTXRXOGE4bTRrVGNMWHByRytXQXEvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoiTGVjdHVyZV8zLnBkZiIsIm5vaWZyYW1lIjpmYWxzZSwidWlkIjoiMjI0OTY1ODciLCJ0cyI6MTU4MDQ4NzY5OTQ0MywieXUiOiIzOTAwNDcxNjExNTc2MjQwMDA2In0%3D Модель Осборна-Сливински самовыдвижения кандидатов на выборах]
 
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|S. Gehlbach. Formal Models in Political Science. Paragraph 1.4.3.
 
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M.J. Osborne, A. Slivinski. A Model of Political Competition with Citizen Candidates. Quarterly
 
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Journal of Economics, vol.111, pp. 65-96 (1993).
 
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|}
 
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=== Семинары и домашние задания ===
 
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{|class='wikitable'
 
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!дата семинара
 
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!задания на семинар
 
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!дедлайн сдачи ДЗ
 
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!домашнее задание
 
 
|-
 
|-
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|17.01
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| || Bayesian statistics II ||  
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|[https://docviewer.yandex.ru/view/0/?*=ZsUr%2BkXE%2B98etL0r0cDTcXM%2FUR17InVybCI6InlhLWRpc2stcHVibGljOi8veDRZWGhHeGVqSzRrRCtCa1l0WkRGT1dPS3hmVXpMdE9Kbk1rQjhrblZOT0pIaDJ0M1NHT1F4Z3cxR0FtcVhvRnEvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoic2VtMS5wZGYiLCJub2lmcmFtZSI6ZmFsc2UsInVpZCI6IjAiLCJ0cyI6MTU3OTM0NzA3ODc3OSwieXUiOiI0MTI4MDg5NDExNTc5MzQ3MDUzIn0%3D Семинар №1]
 
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|24.01
 
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|[https://docviewer.yandex.ru/view/0/?*=E2Wnp1S2VF7%2FJ4i5LcjGTpnZHo97InVybCI6InlhLWRpc2stcHVibGljOi8vMGdVY2dFS3MxdjlyNjd4VFNKRm12dDhGaHNibC91azlqODQ0KzdrWkxjWXNsNllXclZqbko2RW83V21yZldrSXEvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoiaHcxLnBkZiIsIm5vaWZyYW1lIjpmYWxzZSwidWlkIjoiMCIsInRzIjoxNTc5MzQ3MDk1Mjk1LCJ5dSI6IjQxMjgwODk0MTE1NzkzNDcwNTMifQ%3D%3D ДЗ №1]
 
 
|-
 
|-
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|24.01
 
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|[https://docviewer.yandex.ru/view/22496587/?*=6yqYB7IXnzfr5qB8fab5q0zIS0p7InVybCI6InlhLWRpc2stcHVibGljOi8vNnE0L3NkWmZWR0tLUkNyNDdwVWZJWjk3anpXZzcxZTlHYnJoUVNjRURnRHpvMkhFalJ2azdTVDhMV0VHMTBqVXEvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoic2VtMi5wZGYiLCJub2lmcmFtZSI6ZmFsc2UsInVpZCI6IjIyNDk2NTg3IiwidHMiOjE1Nzk4NzM0ODQ5OTQsInl1IjoiMzkwMDQ3MTYxMTU3NjI0MDAwNiJ9 Семинар №2]
 
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|31.01
 
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|[https://docviewer.yandex.ru/view/22496587/?*=tZv9sQu4nzZKRRIHMYYjs7%2FQHxh7InVybCI6InlhLWRpc2stcHVibGljOi8vQXBSTm1YRytrNHRaaHFHQ3I1bllhVzY4U3NUQ0xuMGFMeHNSQ2dFVG0zUWlZVFZBNmlQMHZTdFJrVWdUNWxGbnEvSjZicG1SeU9Kb25UM1ZvWG5EYWc9PSIsInRpdGxlIjoiaHcyLnBkZiIsIm5vaWZyYW1lIjpmYWxzZSwidWlkIjoiMjI0OTY1ODciLCJ0cyI6MTU3OTg3MzU0OTcxMiwieXUiOiIzOTAwNDcxNjExNTc2MjQwMDA2In0%3D ДЗ №2]
 
 
|}
 
|}
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 +
== Software ==
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During this course we will use R as a programming language and RStudio as a GUI.
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How to install R and RStudio?
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1. Download [https://cran.r-project.org/ R] (you can choose another mirror here if you wish) and install it on your computer. Make sure you did it before installing RStudio.
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2. Download [https://rstudio.com/products/rstudio/ 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.
 +
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It is possible avoid installing anything on your PC, using [https://rstudio.cloud rstudio.cloud] (an online version of RStudio).
 +
 +
For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd).
 +
 +
 +
== Homeworks ==
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* Homework 1 (deadline: February 16, 23:59), Chapters 1, 2, 3, and 5 of the [https://www.datacamp.com/courses/free-introduction-to-r DataCamp] course "Introduction to R". Please fill in this [https://docs.google.com/forms/d/e/1FAIpQLSdjgKBM5JSo6D6ajhrWWfFG1ktcKgDfbdK_jQ_ZbW9GwNLzpQ/viewform form]. 
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* Homework 2 (deadline: February 23, 23:59), Chapters 4 and 6 of the [https://www.datacamp.com/courses/free-introduction-to-r DataCamp] course "Introduction to R". 
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After completing the course please provide either the [https://support.datacamp.com/hc/en-us/articles/360001548814-How-can-I-share-my-certificate-Statement-of-Accomplishment- Statement of Accomplishment] or a screenshot of your learning progress via [link TBA]. 
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Deadlines for Homework 1 and 2 are cancelled due to unavailability of the free version of the datacamp online course. Stay tuned!
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* Homework 3 (deadline: February 9, 12:00), Hypothesis testing, binomial test, t-test. [https://github.com/LingData2019/LingData2020/blob/master/hw/hw-pdf/LingData-HW3-comp.pdf HW3 pdf] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW3-comp.html html] [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW3-comp.Rmd Rmd template]
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* Homework 4 (deadline: February 29, 12:00), T-test and ANOVA, reproducing some results from Leivada & Westergaard 2019.
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[https://github.com/LingData2019/LingData2020/blob/master/hw/hw-pdf/LingData-HW4-comp.pdf HW4 pdf] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/hw/hw-html/LingData-HW4-comp.html html] [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW4-comp.Rmd Rmd template]
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 +
== Final project ==
 +
* Projects description [https://github.com/LingData2019/LingData2020/blob/master/projects.pdf link] 
 +
* Projects pre-registration: link to submit your file TBA 
 +
* Final versions of project papers: link to sumbit your files TBA 
 +
 +
 +
== References ==
 +
* Gries, Stefan (2013). Statistics for Linguistics with R : A Practical Introduction (Vol. 2nd revised edition). Berlin: De Gruyter Mouton. [http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=604318 HSE library link]
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* Levshina, Natalia (2015). How to Do Linguistics with R : Data Exploration and Statistical Analysis. Amsterdam: John Benjamins Publishing Company. [http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1093048 HSE library link]
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* Baayen, Harald (2008). Analyzing Linguistic Data: A practical introduction to statistics. Cambridge UP. [http://www.sfs.uni-tuebingen.de/~hbaayen/publications/baayenCUPstats.pdf pdf]
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* Gries, Stefan (2017). Quantitative Corpus Linguistics with R : A Practical Introduction (Vol. Second edition). Milton Park, Abingdon, Oxon: Routledge. eBook
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* Empirical Bayes
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* Harney, H. L. (2016). Bayesian Inference : Data Evaluation and Decisions (Vol. 2nd ed). Springer. eBook 
 +
* McElreath, R. (2016). Statistical Rethinking : A Bayesian Course with Examples in R and Stan. eBook
 +
* ggplot2
 +
* Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. Springer. eBook
 +
* R markdown [https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf Rmd Cheat Sheet
 +
 +
== Course Info ==
 +
 +
This page contains the materials of the course "Linguistic Data: Quantitative Analysis and Visualisation", taught at the HSE Master's program "Computational Linguistics" in 2019-2020 academic year. Modules: 3-4.

Версия 02:36, 17 февраля 2020

  • Instructors: Ilya Schurov and Olga Lyashevskaya

Materials

Data Topics Links
Jan 18 Introduction. Quantitative linguistic research and data types. R basics Intro Slides Lab 01: intro to R
Jan 25 Hypothesis testing. Binomial test. R: dataframes, tydyverse Lab 02 tidyverse cheat sheet
Feb 1 Central limit theorem. Variance. Student's t-test. R: simulating data, boxplots, density plots, binomial test, t-test

Lab 03: Rmd html Viz. distributions

Feb 8 Two-sample t-test. Paired t-test. Confidence intervals. Non-parametric tests Lab 04: Rmd pdfCI slides CI demo
Feb 15 ANOVA. Correlations Lab 05: [Rmd] [pdf]
Feb 22 Tests for categorial data. Chi-squared test. Fisher exact test. Effect size Common statistical tests & linear models
Feb 29 Linear regression. Multivariate linear regression. Dummy variables
Dimensionality reduction. PCA. MDS. t-SNE
CA, MCA. Clusterization
Logistic regression. Model selection
Fixed and random effects. Linear mixed-effects models
Bootstrap. Decision trees. Decision forests
Bayesian statistics
Bayesian statistics II

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 rstudio.cloud (an online version of RStudio).

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


Homeworks

  • Homework 1 (deadline: February 16, 23:59), Chapters 1, 2, 3, and 5 of the DataCamp course "Introduction to R". Please fill in this form.
  • Homework 2 (deadline: February 23, 23:59), Chapters 4 and 6 of the DataCamp course "Introduction to R".

After completing the course please provide either the Statement of Accomplishment or a screenshot of your learning progress via [link TBA]. Deadlines for Homework 1 and 2 are cancelled due to unavailability of the free version of the datacamp online course. Stay tuned!

  • Homework 3 (deadline: February 9, 12:00), Hypothesis testing, binomial test, t-test. HW3 pdf html Rmd template
  • Homework 4 (deadline: February 29, 12:00), T-test and ANOVA, reproducing some results from Leivada & Westergaard 2019.

HW4 pdf html Rmd template

Final project

  • Projects description link
  • Projects pre-registration: link to submit your file TBA
  • Final versions of project papers: link to sumbit your files TBA


References

  • Gries, Stefan (2013). Statistics for Linguistics with R : A Practical Introduction (Vol. 2nd revised edition). Berlin: De Gruyter Mouton. HSE library link
  • Levshina, Natalia (2015). How to Do Linguistics with R : Data Exploration and Statistical Analysis. Amsterdam: John Benjamins Publishing Company. HSE library link
  • Baayen, Harald (2008). Analyzing Linguistic Data: A practical introduction to statistics. Cambridge UP. pdf
  • Gries, Stefan (2017). Quantitative Corpus Linguistics with R : A Practical Introduction (Vol. Second edition). Milton Park, Abingdon, Oxon: Routledge. eBook
  • Empirical Bayes
  • Harney, H. L. (2016). Bayesian Inference : Data Evaluation and Decisions (Vol. 2nd ed). Springer. eBook
  • McElreath, R. (2016). Statistical Rethinking : A Bayesian Course with Examples in R and Stan. eBook
  • ggplot2
  • Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. Springer. eBook
  • R markdown [https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf Rmd Cheat Sheet

Course Info

This page contains the materials of the course "Linguistic Data: Quantitative Analysis and Visualisation", taught at the HSE Master's program "Computational Linguistics" in 2019-2020 academic year. Modules: 3-4.

Источник — http://math-info.hse.ru/wiki2019-20/index.php?title=Математические_модели_политэкономии&oldid=96