This GitHub serves as a repository for STT 802 and EPI 853B
Team:
- Developer: Gustavo de los Campos
- Contributors:
- Gustavo de los Campos
- Grace Hong (Fall 2018 and 2019)
- Andriana Mandousidaki (Fall 2023)
- Yue Xing (Fall 2024)
- Harish Neelam (Fall 2024)
- Current maintainers:
- Gustavo de los Campos
- Yue Xing
Instructors 2026: Gustavo de los Campos (gustavoc@msu.edu)
Time & Place M&W 3:00-4:20PM E111 Fee Hall (In-person).
| Topics | Materials | In-class | Homework |
|---|---|---|---|
| Introduction to R | |||
| Types, basic operations, arrays | R Intro | INCLASS-1 | |
| Reading/Writing data, Descriptive analysis | Read/Write/ Descriptive statistics & basic plots | INCLASS-2 | |
| Loops and conditional statements, functions | Conditionals / Loops / functions | INCLASS-3 | |
| Reporting using RStudio/RMarkdown | Libraries and Distributions / RMarkdown 1/ RMarkdown 2/ cheatsheets | ||
| Linear Algebra | |||
| Linear Algebra in R | Matrix operations | INCLASS-4 INCLASS-5 | |
| Least Squares problems | |||
| Linear Regression | OLS-Handout , OLS Using lm and Matrix operations , Rmarkdown practice | INCLASS-6 INCLASS-7 INCLASS-8 INCLASS-9 | HW 1 |
| Solving linear systems using matrix factorizations | Handout | INCLASS 10 | |
| Maximum Likelihood | |||
Estimation and inference using the optim function |
Logistic Regression handout / ML Bernoulli | INCLASS 11 | INCLASS 12 |
| Non-linear least squares | NLS Handout | INCLASS 13 | |
| Monday, October 19, Midterm | Covers up to non-linear least squares | ||
| Monday October 26, no-class, university break | |||
| Sampling random variables | |||
| Univariate distributions (the 'd', 'p', 'q' and 'r' functions) | Distributions | ||
| Transformation of RVs, Inverse Probability Method, Composition Sampling, and Gibbs Sampler | Sampling RVs handout | INCLASS 14 ; INCLASS 15 | |
| Multivariate normal distribution | Sampling RVs handout / Examples | INCLASS 16 | |
| Power Analysis | Slides / Handout | INCLASS 17 | HW3 |
| Bootstrap | HANDOUT/ Efron & Hastie (2017) / Efron's video/ An example using the boot R-package | ||
| Permutation analysis | Permutation | ||
| Large scale hypothesis testing | Slides / Handout / Ch. 15, Efron & Hastie (2017) | HW3 | |
| Cross-validation | CV Handout | ||
| High-Dimensional Regression, Forward Regression | Handout | HW4 | |
| Penalized Regression | |||
| Tree-based Algorithm, XGBoost | (Not in the final exam) Textbook Chapter 8 | ||
| Neural Networks and Deep Learning | (Not in the final exam) Chapter 10 | ||
| Week before final exam | Mon: Revision, Wed: Open to questions and review of topics | ||
| Final Exam: | Mon. Dec. 8 final exam at Fee Hall E111 3 pm-5 pm |