SYS 6021
Statistical Modeling I
Course Description
This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Course lectures concentrate on theory and practice.
Instructors
Laura Barnes
Fall 2025
FrSa 9:00am - 1:00pm
—
Rating
—
Difficulty
3.64
GPA
Seokhyun Chung
Fall 2025
TBA
—
Rating
—
Difficulty
3.66
GPA
- -
Fall 2024
TBA
—
Rating
—
Difficulty
—
GPA
Julianne Quinn
Fall 2023
TBA
—
Rating
—
Difficulty
3.71
GPA
Sonia Baee
Fall 2021
—
Rating
—
Difficulty
—
GPA
Jonathan Hughes
Spring 2020
—
Rating
—
Difficulty
—
GPA
Jamey Thompson
Spring 2019
—
Rating
—
Difficulty
—
GPA
Alicia Nobles
Fall 2017
—
Rating
—
Difficulty
—
GPA
Abigail Flower
Fall 2015
—
Rating
—
Difficulty
—
GPA
Donald Brown
Fall 2014
—
Rating
—
Difficulty
3.23
GPA
Ginger Davis
Fall 2009
—
Rating
—
Difficulty
3.52
GPA
Frank Deviney
Fall 2009
—
Rating
—
Difficulty
—
GPA