Stats 413: Applied Regression Analysis

Lectures:

B844 East Hall, MW 2:30–4pm

Labs:

G444C Mason Hall, F 9–10am, 10–11am
G444B Mason Hall, F 2–3pm

Instructor:

Yuekai Sun

GSI's:

Zoe Rehnberg, Yumu Liu, Xinzhou Guo

Office hours:

271 West Hall, Tu 5:30–7pm
Science Learning Center, MW 1–2:30pm, Tu 3–4:30pm

Please see the syllabus for other course information.

Notes and references

Date Topics References
Sep 6 supervised learning, linear regression
Sep 8 linear algebra review linalg.PDF
Sep 11 ordinary least squares (OLS)
Sep 13 OLS as MLE, hypothesis testing
Sep 15 R bootcamp ISL §2.3
Sep 18 hypothesis testing under normality
Sep 20 the \(t\)-test, the bootstrap bootstrap.PDF
Sep 22 linear regression lab ISL §3.6
Sep 25 bootstrap confidence intervals
Sep 27 classical linear model
Sep 29 qualitative predictors ISL §3.3.1, §3.6.6
Oct 2 OLS as MoM, geometry of least squares
Oct 4 finite-sample properties of OLS
Oct 6 midterm I review
Oct 9 Gauss-Markov theorem
Oct 11 midterm I
Oct 13 bootstrap lab ISL §5.3.4
Oct 18 convergence of random variables
Oct 20 midterm I recap

ISL refers to the textbook An Introduction to Statistical Learning.

Lecture notes on linear regression

linreg1.PDF

linear regression, OLS, hypothesis testing under normality

linreg2.PDF

classical linear model, finite-sample properties of OLS

linreg3.PDF

convergence of random variables, asymptotic properties of OLS

Supplemental notes

mvn.PDF

the multivariate Gaussian/normal (MVN) distribution

R resources

There are plenty of R resources online. The RStudio online learning page is a good place to start. If you find a resource that is particularly helpful, please let me know so that I may share it with the class.

Pre-labs and problem sets

Due date Assignment Data files
Sep 15 pset0.PDF
Sep 15 plab1.PDF
Sep 22 plab2.PDF
Sep 29 pset1.PDF Boston.CSV, n88_pol.CSV
Sep 29 plab3.PDF
Oct 13 plab4.PDF
Oct 18 pset2.PDF penn.CSV

Please check Canvas for solutions to the problem sets.