Cogs 243: Statistical Inference and Data Analysis (Winter 2017)

Monday 1:00-3:50, CSB 003

Course website:

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Displayed course content is preliminary; details may change before the quarter begins.
(Thu Oct 20, 2:56 p.m.)


Angela Yu (Professor)

/ phone: 858-822-3317

Office Hours: M 4-5, Thr 2-3 in SSRB 246

Chaitanya Ryali (TA)

Office Hours: W 1-3 in SSRB 247B

Shradha Agrawal (TA)

Office Hours: F 12-2 in SSRB 247A

Course Description

This course provides a rigorous treatment of hypothesis testing, statistical inference, model fitting, and exploratory data analysis techniques commonly used in the sciences, social sciences, engineering, medicine, finance, and other fields dealing with data. Both frequentist and Bayesian techniques will be covered. Students will acquire an understanding of mathematical foundations and hands-on experience in applying these methods to real data using Matlab.

Students are expected to be comfortable with calculus, linear algebra, and elementary probability (e.g. Ch. 1-6 of the course textbook), as well as having fluency in a programming language (Matlab, Python, C, Java, etc.). All HW assignments will be in Matlab. There will be an optional Matlab tutorial in the first week for those who need an intro/refresher.

There is an undergraduate version of this course (Cogs 118D) that is also offered this quarter. The material covered will be similar, but 243 expects students to be more self-sufficient and collaborative.

We will use Piazza for class discussion (which contributes toward your participation grade): . If you have any problems or feedback for the developers, email

Required textbook: "Probability and Statistics" by Degroot & Schervish (2012), 4th Ed.


Additional readings and references will be posted on the schedule below.

Course Details and Policies


  • 25% participation
  • 24% demos
  • 25% HW assignments
  • 25% final project
  • 1% self-test (HW 1)
If taken for 2 units, no final project is required (all other participation expected to be the same)

UCSD Policy on Integrity of Scholarship

Course Schedule

Week Date Topic Readings Assignments
1 Mon Jan 09 Introductions
Review of probability
Review of linear algebra
Review of Matlab
Ch. 1-4, 5.1, 5.6, 5.8, 6.1-6.3
Demo group composition due 1/30
HW 1 due 1/18

3 Mon Jan 23 Estimation: Bayesian
Estimation: Frequentist
Ch. 7.1-7.5, 7.7
Ch 7 demo due

4 Mon Jan 30 Sampling Distribution of Estimators
Ch. 8.1-8.7
Ch 8 demo due
Ch 7 HW due Wed
Project general info (members, topic, data set) due

5 Mon Feb 06 Review

Project outline due
Ch 8 HW due Thur

6 Mon Feb 13 Hypothesis Testing 9.1, 9.5-9.9
Ch 9 demo due
Ch 9 HW due Fri
Project progress report due 02/22

8 Mon Feb 27 Regression
Ch. 11.1-11.6 Ch 11 Demo due
Ch 11 HW due Fri

9 Mon Mar 06 Model selection/comparison
Ch. 10.1-10.4 Ch 10 demo due
Project prelim slides due Thursday

10 Mon Mar 13 Final Project Presentation

Project finalized slides due
Project report due Thursday