STAT 757 — Fall 2020
Time series of the difference in Arctic sea ice extent in March (the month of ice-extent maximum) and September
(the month of ice-extent minimum) from the mean values for the time period 1979-2007. Based on a least squares
linear regression, the rate of decrease for the March ice extents was 2.8% per decade and and September was 11.3% per decade.
Courtesy of NOAA - J. Richter-Menge, S. Nghiem, D. Perovich, I. Rigor (Public domain),
via Wikimedia Commons
Introduction
This is the course web page for Applied Regression Analysis STAT 757 FALL 2020 – Section 1001. Please see the menus below for class resources, the up-to-date class schedule, assignments and solutions to homework and quizes.
Course syllabus
Modules, assignments and grades will be posted through Web Campus.
Course information
- Faraway, J. Linear models with R, Second Edition. Chapman and Hall/CRC, 2016.
- Kutner, M. et al. Applied Linear Statistical Models 5th Edition / Applied Linear Regression Models 4th Edition
- Review of statistical concepts, calculus and matrices.
- R is required for the class. Students can get an installation file from the R project (free and open source software).
- Students are also required to install LaTeX (free typesetting language). This is required to compile R Markdown, and is the standard for writing mathematical and statistical reports and publications.
- Students are recommended to use the environment R-Studio (free integrated development environment).
- Free scientific programming lessons are available through Software Carpentry (nonprofit teaching programming for researchers).
- Paul Hurtado's tutorials and R resources
- Advanced students interested in the theory of programming are recommended Eloquent Javascript (free, creative commons).
The up-to-date schedule is in the table below.
| Week | Lecture slides | Activities | Homework, Quizzes, Midterms and Final |
|---|---|---|---|
| 08/24 - 08/28 | Introduction to scripting in R | Activity sheets: Supplementary materials: |
Homework 1:
|
| 08/31 - 09/04 | Simple linear regression | Supplementary Materials: |
Quiz 1:
|
| 09/07 - 09/11 |
Simple linear regression
| Supplementary Materials: |
Quiz 2:
|
| 09/14 - 09/18 | Analysis of variance and a matrix approach to simple regression | Supplementary Materials: |
Quiz 3:
|
| 09/21 - 09/25 | Midterm and review
|
Supplementary Materials: | Midterm 1:
|
| 09/28 - 10/02 | Multiple regression | Supplementary Materials: |
Quiz 4:
|
| 10/05 - 10/09 | Inference | Supplementary Materials: |
Quiz 5:
|
| 10/12 - 10/16 | Prediction and explanation | Supplementary Materials: |
Midterm 2:
|
| 10/19 - 10/23 | Diagnostics | Supplementary Materials: | |
| 10/26 - 10/30 | Diagnostics | Supplementary Materials: | |
| 11/02 - 11/06 | Linear and nonlinear transformations | Work on midterm project | |
| 11/09 - 11/13 | Model selection
|
Review week -- catch up on topics before final project | |
| 11/16 - 11/20 | Categorical predictors and a complete example |
Final project:
|
|
| 11/23 - 11/27 | Missing data and introduction to data assimilation | ||
| 11/30 - 12/04 | Final project review sessions | ||
| 12/07 - 12/11 | Final project review sessions |
|