Stats 413: Applied Regression Analysis


B844 East Hall, MW 2:30–4pm


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


Yuekai Sun


Zoe Rehnberg, Yumu Liu, Xinzhou Guo

Office hours:

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

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 13 bootstrap lab ISL §5.3.4
Oct 18 convergence of random variables
Oct 20 midterm I recap
Oct 23 serial dependence
Oct 25 general linear model
Oct 27 returns to scale in electricity supply Nerlove's paper
Oct 30 asymptotic properties of OLS
Nov 1 the \(z\)-test, testing linear hypothesis
Nov 3 the US T-bills market tbills.PDF
Nov 6 power and sample size calculations
Nov 10 Are markets efficient? Mishkin's paper
Nov 13 midterm II review
Nov 17 neural networks
Nov 20 testing linear hypothesis, estimating \({\sf Avar}\bigl[\hat{\beta}_n\bigr]\)
Nov 22 support vector machines
Nov 27 bias-variance trade-off, cross validation ISL §2.2, §5.1
Nov 29 high-dimensional regression ISL §6.2
Dec 1 ridge regression, lasso ISL §6.6
Dec 4 lasso, logistic regression ISL §4.1–4.3
Dec 6 exponential families, generalized linear models glm.PDF
Dec 8 logistic regression ISL §4.6
Dec 11 tree-based methods ISL §8.1, §8.2

ISL refers to the textbook An Introduction to Statistical Learning.

Lecture notes on linear regression


linear regression, OLS, hypothesis testing under normality


classical linear model, finite-sample properties of OLS


convergence of random variables, general linear model, asymptotic properties of OLS


testing linear and non-linear restrictions, estimating the asymptotic variance

Supplemental notes


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
Oct 27 lab5.PDF nerlove.CSV
Nov 3 pset3.PDF nerlove.CSV
Nov 10 plab6.PDF mishkin.CSV
Nov 27 pset4.PDF mishkin.CSV
Dec 1 prelab7.PDF
Dec 8 plab8.PDF
Dec 11 pset5.PDF logregPredictors.txt, logregResponse.txt

Please check Canvas for solutions to the problem sets.