Linguistic Data: Quantitative Analysis and Visualisation: linguistic theory: различия между версиями
(не показано 16 промежуточных версий этого же участника) | |||
Строка 14: | Строка 14: | ||
| [https://gist.github.com/ischurov/19c8d698a87af3e723e63452a79ffd09 script] | | [https://gist.github.com/ischurov/19c8d698a87af3e723e63452a79ffd09 script] | ||
| [https://youtu.be/E8zlljU7A-o lecture], [https://youtu.be/wlu-aYLPxRs practice] | | [https://youtu.be/E8zlljU7A-o lecture], [https://youtu.be/wlu-aYLPxRs practice] | ||
+ | |- | ||
+ | | Jan 25 | ||
+ | | Statistical hypothesis testing. Binomial test. | ||
+ | | | ||
+ | | [https://youtu.be/CztjP3mwbPA lecture], [https://youtu.be/UcryMfKg5EQ practice] | ||
+ | |- | ||
+ | | Feb 1 | ||
+ | | Estimate of mean. Central limit theorem | ||
+ | | | ||
+ | | [https://youtu.be/xQOpjc2yvw4 video] | ||
+ | |- | ||
+ | | Feb 8 | ||
+ | | One sample t-test | ||
+ | | [https://github.com/ischurov/LingDataQAV-2021/blob/master/lesson05-one-sample-t-test.Rmd seminar Rmd] [https://htmlpreview.github.io/?https://github.com/ischurov/LingDataQAV-2021/blob/master/lesson05-one-sample-t-test.html preview] | ||
+ | | [https://youtu.be/ZB4b5LlAPnw video] | ||
+ | |- | ||
+ | | Feb 15 | ||
+ | | Review | ||
+ | | [https://github.com/ischurov/LingDataQAV-2021/blob/master/lesson06-review.Rmd Rmd], [https://htmlpreview.github.io/?https://github.com/ischurov/LingDataQAV-2021/blob/master/lesson06-review.html preview] | ||
+ | | [https://youtu.be/WX1qjU5QAf0 video] | ||
+ | |- | ||
+ | | Feb 27 | ||
+ | | Two-sample t-test. One-sided and two-sided alternatives | ||
+ | | | ||
+ | | [https://youtu.be/Jm5Wue906zg video] | ||
|} | |} | ||
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Due date: 2021-01-31 23:00 MSK. Late penalties are applied to each chapter independently. | Due date: 2021-01-31 23:00 MSK. Late penalties are applied to each chapter independently. | ||
+ | |||
+ | === Homework #2 === | ||
+ | * [https://github.com/ischurov/LingDataQAV-2021/blob/master/homeworks/hw2.pdf Homework file] | ||
+ | * Please, put your solutions in the [https://github.com/ischurov/LingDataQAV-2021/blob/master/homeworks/hw2.Rmd related Rmd] file. | ||
+ | * Then upload it [https://www.dropbox.com/request/vUrpx6b8zsLPL3tcTnjt here] | ||
+ | |||
+ | Due date: 2021-02-14 23:00 MSK. | ||
+ | |||
+ | === Homework #3 === | ||
+ | * [https://github.com/ischurov/LingDataQAV-2021/blob/master/homeworks/hw3.pdf Homework file] | ||
+ | * Please, put your solutions in the [https://github.com/ischurov/LingDataQAV-2021/blob/master/homeworks/hw3.Rmd related Rmd] file. | ||
+ | * Then upload it [https://www.dropbox.com/request/HouJmsZNfxVYvxQ559fb here] | ||
+ | |||
+ | Due date: 2021-03-16 23:00 MSK | ||
== Software == | == Software == | ||
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For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd). | For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd). | ||
+ | |||
+ | == Final project == | ||
+ | * [https://github.com/ischurov/LingDataQAV-2021/blob/master/final-projects.pdf Final projects description] | ||
+ | * Project proposals: please, upload [https://www.dropbox.com/request/w8ZfbzbebzTpmUdJE2bA here]. | ||
== Online course == | == Online course == |
Версия 23:49, 3 апреля 2021
- Instructors: Ilya Schurov and Ivan Pozdnyakov
Содержание
Materials
Data | Topics | Links | video |
---|---|---|---|
Jan 11 | Introduction. Quantitative linguistic research and data types. R basic | notebook | lecture, practice |
Jan 18 | Measures of statistical dispersion. Variance and standard deviation. | script | lecture, practice |
Jan 25 | Statistical hypothesis testing. Binomial test. | lecture, practice | |
Feb 1 | Estimate of mean. Central limit theorem | video | |
Feb 8 | One sample t-test | seminar Rmd preview | video |
Feb 15 | Review | Rmd, preview | video |
Feb 27 | Two-sample t-test. One-sided and two-sided alternatives | video |
Homeworks
Academic ethics policy: you have to do your homeworks by yourself. In case of academic cheating (e.g. if you copy someone else's work, etc.), your work will receive grade 0 and the program supervisor will be notified. If you feel that you are stuck with the homeowork, ask for instructor's advices and hints.
Late penalties: in case of late submission, your grade will be multiplied by exp(-t / 86400), where t is the number of seconds since the due date. For example, if you delay the submission by one day, your grade will be multiplied by exp(-1)=0.3678794412.
Extensions: you can ask for up to two extensions of homework due dates during the course. Each extension is one week. Extensions due to valid excuses (i.e. illness) do not count.
Homework #1
Complete the following chapters in DataCamp Introduction to R course:
Due date: 2021-01-31 23:00 MSK. Late penalties are applied to each chapter independently.
Homework #2
- Homework file
- Please, put your solutions in the related Rmd file.
- Then upload it here
Due date: 2021-02-14 23:00 MSK.
Homework #3
- Homework file
- Please, put your solutions in the related Rmd file.
- Then upload it here
Due date: 2021-03-16 23:00 MSK
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).
Final project
- Final projects description
- Project proposals: please, upload here.
Online course
We will use some parts of DataCamp online course Introduction to R. (Will be made available free for the students.)
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. link