Schedule

STAT 410: Linear Regression (Fall 2017)

Date	Topics	Readings	Slides (handout)	Homework	Lab
8/22	Introduction	1.1-1.3	Lecture1
8/24	Parameter estimation (SLR)	2.2.1, 2.2.3, 2.11	Lecture2 \| delivery.R & data
8/29&8/31	UNIVERSITY CLOSES (HARVEY)			Homework1
9/5, 9/7	Properties of estimators in SLR	2.2, 2.3, 2.4; Appendix C.2	Lecture 3		Lab1 (Intro. to R)
9/12	Real data practice	Lecture1-3	Lecture4 (R Markdown)		Lab2
9/14&9/19	Multiple linear regression	3.1, 3.2.1-3.2.4	Lecture5	Homework 2 (Canvas)	No lab
9/21	Multiple linear regression (inference)	3.3-3.5	Lecture6
9/26	ANOVA	2.3.2, 2.3.3, 3.3.1	Lecture7		Lab 3
9/28	General linear hypothesis and Simultanesous CI	3.3,4, 3.4.3	Lecture8
10/3	Midterm Review		Midterm Review
10/5	Exam 1
10/10	NO CLASS
10/12	Model diagnostics I	4.2.1, 4.2.2, 4.2.3	Lecture9 Example-Lecture9-Residual_Plot
10/17	Model diagnostics II	6.1, 6.2, 6.3, 6.4	Lecture10		Lab 4
10/19	Midterm 1 & Project
10/24	Mixed model	5.5, 5.6	Lecture11
10/26	Categorical analysis	8.1, 8.2, 8.3	Lecture12
10/31 11/2	Multicollinearity	9.1, 9.2, 9.3, 9.4	Lecture13 Lecture13: R markdown bridge-dataset		Lab 5
11/7	Model selection	Ch. 10	Lecture14 Lecture14: R markdown
11/9	Logistic regression	Ch.13	Lecture15 MichelinNY Lecture15
11/14	Presentation I
11/16	Presentation II
11/21	Penalized regression: Ridge & LASSO	Reference: Chapter 3 of The Elements of Statistical Learning by Hastie, Tibshirani and Friedman	Lecture16 Ten Simple Rules for Effective Statistical Practice
11/23	NO CLASS
11/28	Wrap-up				Lab6
11/30	Exam 2