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 uni-potsdam.de
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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Linear Modeling (Summer Semesters, MSc Cognitive Systems, MSc IECL)

Anyone can attend this course; there is no need to email me to ask whether you can attend. You can only get credit if you are in the MSc program in Cognitive Systems, or the IECL program. Please see the PULS FAQs to find out how the sign-up system works (in German).
This course is required for the MSc Cognitive Systems program, and for advanced statistical data analysis in psycholinguistics. We will cover the basic theory of generalized linear (mixed) models. We will be using the software R and RStan.
Times, location: Wednesdays 10:15-11:45AM, II.14.009 (Golm, Haus 14, room 009).
Prerequisities: Participants should have some knowledge of calculus and basic linear algebra (see: Foundations of Mathematics, which is taught every winter semester).
Textbooks: We will use three textbooks and several articles:
  1. We will need an introductory textbook on R: The art of R programming.
  2. An Introduction to Generalized Linear Models, Third Edition, by Dobson and Barnett. See here. Please also download data, and errata. Solutions to selected exercises are available here.
  3. Data Analysis Using Regression and Multilevel/Hierarchical Models (Analytical Methods for Social Research), Gelman and Hill, June 2007 edition. See here. You can access code here.
  4. Please read these two introductory articles for a general overview:Vasishth and Nicenboim 2016 (also see code) and Nicenboim and Vasishth 2016.
Lecture notes: The current version is on Github.
Homework: Several assignments will be handed out. These are not graded but solutions will be discussed in class.
Moodle2 site: here. Schedule:
Lecture Topic Reading HW (always due one week later)
(1) Apr 13 Intro to R Chs 2-3 of Matloff HW 0
(2) Apr 20 Intro to R Ch 4-5 of Matloff HW 1
(3) Apr 27 Intro to R Ch 6-8 of Matloff HW 2
(4) May 4 Introduction to linear regression Gelman and Hill Ch 3 HW 3
(5) May 11 Introduction to regression (contd) Gelman and Hill Ch 4 HW 4
(6) May 18 In-class exercises -
(7)May 25 Multiple linear regression HW 5
(8) June 1 Generalized linear models GH Ch 5 HW 6
(9) June 8 Linear mixed models GH Ch 13 HW 7
(10) June 15 Introduction to Bayesian statistics Dobson Ch 12, 13 HW 8
(11) June 22 Bayesian linear regression Dobson Ch 14 HW 9
(12) June 29 Bayesian linear mixed models Sorensen et al 2015 HW 10
(13)July 6 HW discussions
(14) July 13 In-class exercises
(15) July 20 Exam (50 minutes)

Grading: The final grade is based on a 50-minute written exam.