Welcome to PSYCH-GA 2229 Regression (2024 Spring)

Welcome to PSYCH-GA 2229 Regression (2024 Spring)#

This is the course website for PSYCH-GA 2229 Regression, taught by Professor Madalina Vlasceanu, Ph.D., at New York University. The content on this site is provided Open Accessible under the CC BY-NC-SA 4.0 License.

Goals#

Students completing this doctoral-level course will have a detailed understanding of multiple regression (MR) as a data-analytic method. Students will review theory and practice of the General Linear Model and learn how MR can be used to carry out analyses of quantitative and categorical data. The relation of MR to correlation, t-tests, ANOVAs, and Mixed Models will be made explicit. Students will solve practical problems in estimating and testing regression models and they will gain experience in carrying out MR analyses using R and Python.

Useful information#

Instructor: Prof. Madalina Vlasceanu, Ph.D. mov209@nyu.edu
Office: Meyer 505 [office hours by appointment]

Teaching assistant: Kareena del Rosario kareena.delrosario@nyu.edu
Office: Meyer 521 [office hours by appointment]

Lecture: Mondays and Wednesdays 2–3pm in Meyer 433 or on zoom
Lab: Mondays and Wednesdays 3–3:50pm in Meyer 433

Acknowledgements#

This course created by Prof. Madalina Vlasceanu at Department of Psychology at New York University. The website is built by Ke (Kay) Fang, a master student at NYU Collective Cognition Lab, using Juypter Book.

Schedules#

Day	Date	Lecture	Lab	Readings	Assignments
1	Jan 22	Lecture 1: Why learn Statistics?	Installing Python	Ch 1
2	Jan 24	Lecture 2: Intro to Python	Intro to R	Ch 2
3	Jan 29	Lecture 3: Data Visualization	Data manipulation in R	Ch 5
4	Jan 31	Lecture 4: Probability	Data visualization in R	Ch 9
5	Feb 5	Lecture 5: Correlations in Python		Ch 10
6	Feb 7	SPSP – NO CLASS
7	Feb 12	Lecture 6: Hypothesis Testing		Ch 11
8	Feb 14	Lecture 7: Chi-square test & T-Test		Ch 12
9	Feb 19	PRESIDENTS’ DAY – NO CLASS		Ch 13
10	Feb 21	Lecture 8: ANOVA in Python		Ch 14	A.1 due
11	Feb 26	Lecture 9: ANOVA in Python, Continued		Ch 15
12	Feb 28	Lecture 10: Regression in Python
13	Mar 4	Lecture 11: Moderation/Interaction
14	Mar 6	Lecture 12: Regression Assumptions
15	Mar 11	Lecture 13: Mediation in Python		Blair, 2020	A.2 due
16	Mar 13	Lecture 14: Nonlinear Regression		Gureckis, 2021
17	Mar 18	SPRING BREAK – NO CLASS
18	Mar 20	SPRING BREAK – NO CLASS
19	Mar 25	Lecture 15: MixedModelsR /LMMPython		Brown, 2021
20	Mar 27	Midterm: Practice content covered so far		Bates, 2005
21	Apr 1	Bayesian Inference (Dr. Joe Bak-Coleman)			A.3 due
22	Apr 3	Power Analysis Simulations (Dr. Jan Voelkel)
23	Apr 8	Computation (Dr. Felicia Loecherbach)
24	Apr 10	Network Analysis (Dr. Seungwoong Ha)
25	Apr 15	Web Scraping (Dr. Ben Guinaudeau)
26	Apr 17	Stat. Models of Behavior (Dr. Ravi Shroff)
27	Apr 22	Practical ML (Dr. Michael Morais)
28	Apr 24	Class project presentation
29	Apr 29	Class project presentation
30	May 1	Class project presentation
31	May 6	Class project presentation			Final Report