STAT 3301: Regression and Statistical Computing
Instructor. Aaron J. Molstad (amolstad@umn.edu)
Office hours. Monday, 10:30 - 11:30AM and Wednesday 2:00 - 3:00PM in Ford Hall 384
Teaching assistant. Rui Zhang (zhan8861@umn.edu)
TA Office hours. Thursday, 1:00 - 3:00PM in Ford Hall 495
Syllabus. [pdf]
Lecture. Monday, Wednesday, Friday at 12:20 - 1:10PM in Civil Engineering Building 212
Lab. Wednesday at 8:00 - 8:50AM in Lind Hall 302
Download R. [CRAN]
Download RStudio. [Rstudio]
Code formatting guidelines. [Google style sheet]
R basics. [pdf]
Note that you must be logged into your UMN Canvas account to access course materials.
| Lecture | Topics | |||
| 0 (9/4) | Introduction to Data Science and Statistics | |||
| 1 (9/6) | Models and random variables [complete] | |||
| 2 (9/9) | Probability functions, expected value and variance [slides][complete][code] | |||
| 3 (9/11) | Parameters, named distributions [slides][code] | |||
| 4 (9/13) | Estimators and estimates, sample mean and variance [slides][code] | |||
| 5 (9/16) | Sample mean and variance, CLT [slides][code] | |||
| 6 (9/18) | QQ-plots and simulation studies [slides][code] |
Homework 1 (Due Friday, September 20th at 4:59PM) [pdf][solutions][R markdown]
Complete Lectures 1-5 [pdf][R markdown]
Lab 1 [pdf][R markdown]
Lab 2 [pdf][R markdown]
| 7 (9/20) | Inversion method, Box Muller method[slides][code] | |||
| 8 (9/23) | Box Muller method, one-sample model [slides][code] | |||
| 9 (9/25) | Confidence intervals, hypothesis testing [slides][code] | |||
| 10 (9/30) | Power, p-value [slides][code] | |||
| 11 (10/2) | T-tests, power curves [slides][code] | |||
| 00 (10/4) | Quiz 1 Review [lecture] |
Lab 3 [pdf][R markdown]
Lab 4 [pdf][R markdown]
Complete Lectures 6-8 [pdf][R markdown]
Complete Lectures 9-10 [pdf][R markdown]
Homework 2 (Due Wednesday, October 2nd at 4:59PM) [pdf][solutions][R markdown]
Practice Quiz 1 [pdf]
Quiz 1 Monday, October 7th at 12:20 - 1:10PM [pdf]
| 12 (10/9) | Confidence intervals for a proportion [lecture] | |||
| 13 (10/11) | Introduction to regression [lecture][code][corn data] | |||
| 14 (10/14) | Introduction to regression continued [lecture][code] | |||
| 15 (10/16) | Notation, least squares [lecture][code][garlic data][gene data] | |||
| 16 (10/18) | Least squares continued [lecture] |
Homework 3 (Due Friday, October 18th at 4:59PM) [pdf][solutions][solutions rmd][Twins data]
Complete Lectures 11-12 [pdf][R markdown]
Complete Lectures 13-16 [pdf][R markdown]
Lab 5 [pdf][R markdown]
Lab 6 [pdf][R markdown]
| 17 (10/21) | Regression examples [lecture][code] | |||
| 18 (10/23) | Regression diagnostics [lecture] | |||
| 19 (10/25) | Properties of OLS [lecture] | |||
| 20 (10/28) | Hypothesis testing in linear models [lecture][code] | |||
| 21 (10/30) | Confidence and prediction intervals [lecture][code] |
Homework 4 (Due Friday, November 1st at 4:59PM) [pdf]solutions][solutions rmd][RHR data][wine data]
Complete Lectures 17-19 [pdf][R markdown]
Complete Lectures 20-23 [pdf][R markdown]
Practice Quiz 2 [pdf]
Quiz 2 Review [pdf][lecture]
Quiz 2 Monday, November 11th at 12:20 - 1:10PM
| 22 (11/1) | Comparing nested models [lecture] | |||
| 23 (11/4) | Testing interactions [lecture] | |||
| 24 (11/13) | R-squared, cross-validation [lecture] |
Homework 5 (Due Friday, November 15th at 4:59PM) [pdf][submit][RHR data][arsenic data]
| 25 (11/15) | Cross-validation [lecture] | |||
| 26 (11/18) | Information criteria [lecture] | |||
| 27 (11/20) | Backwards elimination, ridge regression [lecture][code] |
Complete Lectures 24-26 [pdf][R markdown]
Homework 6 (Due Wednesday, November 27th at 4:59PM) [pdf][submit][divorce][trees][paper]