SYS 6021

Statistical Modeling I

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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.


  • Laura Barnes

     Rating

     Difficulty

     GPA

    3.65

     Sections

    1

    Last Taught

    Fall 2025

  • Seokhyun Chung

     Rating

     Difficulty

     GPA

    3.66

     Sections

    2

    Last Taught

    Fall 2025

  • - -

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2024

  • Julianne Quinn

     Rating

     Difficulty

     GPA

    3.71

     Sections

    Last Taught

    Fall 2023

  • Sonia Baee

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2021

  • Jonathan Hughes

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Spring 2020

  • Jamey Thompson

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Spring 2019

  • Alicia Nobles

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2017

  • Abigail Flower

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2015

  • Donald Brown

     Rating

     Difficulty

     GPA

    3.23

     Sections

    Last Taught

    Fall 2014

  • Frank Deviney

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2009

  • Ginger Davis

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2009