# 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:
GSI's:
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.