Stat 505 - Linear Models
STAT 505 is the first semester in a two semester sequence of courses designed to help you gain a deeper level of understanding of the most common statistical methods used by statisticians. STAT 505 is a course in the theory and application of linear models, the foundation of most models used in statistical analysis. Topics include: Special matrix theory for statistics, multivariate normal distribution, distributions of quadratic forms, estimation and testing for the general linear model, one-way and two-way classification models, contrasts (main effect, simple effect and interaction), and multiple comparison techniques.
By the end of this course, the successful student should be able to:
- Use rigorous mathematical techniques and methods of proof to derive results in matrix theory for statistics.
- Derive estimators, hypothesis tests, and confidence intervals related to linear models.
- Understand the assumptions, uses, and limitations of linear models.
- Use R to fit linear models to data and conduct inference on linear model parameters, with and without using built-in functions.
- Communicate (written, visually, and orally) results of a statistical analysis and statistical concepts in non-technical terms.
Log into D2L course page to access course material, assignments, etc.
- Happy faces and restaurant tips (Example 16.15 in Utts and Heckard, 5th ed.)
- Cloth flammability tests (from Elementary Statistics, 7th ed. by Triola)
- IPEDS data Excel file (Source: https://nces.ed.gov/ipeds/use-the-data)
- R and RStudio basics
- A Beginner's Guide to R
- R cheatsheets (see "Data visualization with ggplot2" and contributed cheatsheet "Base R")
- Datacamp's free Introduction to R Programming online course
- Guru99's free R Tutorial for Beginners
Links to additional R Resources are posted in our D2L Content page.