Shravan Vasishth

Professor, Dept. of Linguistics, University of Potsdam, 14476 Potsdam, Germany
Speaker, Language Cluster, Cognitive Science
Phone: +49-(0)331-977-2950 | Fax: - 2087 | Email: vasishth at
GPG public key, Orcid ID, google scholar, github, bitbucket, statistics blog, vasishth lab blog
Doing a PhD with me: README.1st
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Introduction to Statistical Data Analysis (Summer Semesters, MSc programs)

Introduction: What this course is about

This course provides an introduction to statistical methods for MSc Linguistics (MM5), EMCL, IECL, IDEALAB. Please see the PULS FAQs to find out how the sign-up system works (in German). IECL and EMCL students will have separate sign-up sheets; they do not have to sign up on PULS.
We will be using the software R, and RStudio, so make sure you install these on your computer. Topics to be covered:
  1. Very basic R usage, basic probability theory, random variables, including jointly distributed RVs, probability distributions, including bivariate distributions, Maximum Likelihood Estimation, sampling distribution of mean
  2. Null hypothesis significance testing, t-tests, confidence intervals, type I error, type II error, power, type M and type S errors
  3. An introduction to (generalized) linear models
  4. An introduction to linear mixed models
Times, location: At Golm campus, Potsdam: Mondays 2:15-3:45PM, II.14.222, CIP (computer) Pool (Haus 14 2nd floor).
Lecture notes: The slides for this course are available here. We will use three sets of notes available online:
  1. We will need an introductory reference textbook on R: The art of R programming. You can buy a copy of the published book, or use the online pdf.
  2. Dan Navarro's book.
  3. My own lecture notes, see here. To download the pdf, click on raw.
I strongly advise students planning to take this course to work through the first two introductory R courses on datacamp.
Homework: During the first weeks of the course, three homework assignments will be given out; these are on R programming. The homework for the statistics part of the course is provided through To do the homework, (a) first sign up here as a team member, and (b) then go here to do the exercises. These are automatically checked on datacamp, and do not count for the final grade. I will be able to see your final score.
Grading: Completing one in-class quiz with 50% or more marks gives you 30 percent of the final grade. A 90 minute written exam will count for the remaining 70 percent.
Previous year's exam: 2015.
Quiz date: 6th June, during class.
Final exam date, time and place: 20 July (Wednesday) 12-2PM, Haus 28, Room 108.
Moodle2 website: All communications with students in Potsdam will be done through this website.


Lecture Video Reading Homework
Intro R lectures 1-4 No video Chs 2-8, Matloff HW1-3
Lecture 1 video to be added Basics exercises
Lecture 2 video to be added Hypothesis testing exercises
Lecture 3 video to be added Linear models
Lecture 4 video to be added Linear mixed models
In-class exercises No video - -