Regression Analysis (350-0-22)
Instructors
Arend Matthew Kuyper
IPR, 2040 Sheridan Road, Evanston
Meeting Info
Technological Institute L251: Tues, Thurs 11:00AM - 12:20PM
Overview of class
This is an applied regression course. We will discuss statistical estimation and inferential techniques such as least-squares, confidence intervals, and hypothesis tests, regarding both the regression parameters and the error variance. We will study the regression models by specifying what the underlying assumptions are, how to check them through diagnostics, and how to build models based on data. Homework will be assigned weekly or biweekly (about 7 assignments). One close-book midterm exam (one double-sided formula sheet is allowed); One open-book final exam/project.
Registration Requirements
Prerequisite or co requisite: STAT 320-1. Some basic familiarity with R is helpful, but not entirely necessary (see suggested materials for resources for R).
Learning Objectives
By the end of the class students are expected to (1) formulate statistical questions for a real life problem; (2) use visualization techniques to explore the data; (3) choose the appropriate statistical methods and justify the choice; (4) perform regression analysis using R programming; (5) describe and present the data analysis results.
Teaching Method
A typical class will devote about 15-30 minutes to discussion/lecture with the remaining time devoted to working on activities and homework where students will either work by themselves or in groups. Students will be expected to adequately prepare for each discussion/lecture by reviewing assigned material (e.g. readings, videos, etc…) because the majority of class time will be spent working on activities/homework which will be designed around the assigned material. Students will be expected to collaborate and engage with other students to help each other learn and solve problems. We will be using R and RStudio for conducting and communicating statistical analyses and concepts.
Evaluation Method
Students will be evaluated through small assignments, weekly homework assignments, a midterm exam, and a final exam/project.
Class Materials (Required)
(1) Applied Regression Analysis and Generalized Linear Models Third Edition, By John Fox, ISBN-10: 1452205663, ISBN-13: 978-1452205663
(2) Laptop for in class activities/homework — contact department if access to a laptop is an issue.
(3) Free statistical software R (https://cran.rstudio.com/)
(4) Free integrated development environment software RStudio (https://www.rstudio.com/). Think of R as the car engine needed to power and run everything while RStudio is the steering wheel/dashboard that we use to run and control the car.
Class Materials (Suggested)
(1) While we will be developing a free online R companion book to go with the course, the author has developed one and students may want to purchase this as an extra resource: An R Companion to Applied Regression 3rd Edition, By John Fox and Sanford Weisberg, ISBN-10: 1544336470, ISBN-13: 978-1544336473
(2) Free online textbook, Tidy Modeling with R: https://www.tmwr.org/
(3) Free online textbook, Introduction to Statistics and Data Science:
(4) R & Python Learning Resources on Departments website: https://nustat.github.io/intro-stat-data-sci/ https://statistics.northwestern.edu/undergraduate/r_python_resources.html
Class Attributes
Formal Studies Distro Area