SYS 4021

Linear Statistical Models

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Course Description

Pre-Requisite(s): CS 2100, APMA 3100 and APMA 3120

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.


  • Julianne Quinn

     Rating

     Difficulty

     GPA

    3.68

     Sections

    Last Taught

    Fall 2024

  • Laura Barnes

     Rating

    2.56

     Difficulty

    2.83

     GPA

    3.34

     Sections

    Last Taught

    Fall 2022

  • Ginger Davis

     Rating

     Difficulty

     GPA

    3.07

     Sections

    Last Taught

    Fall 2009

  • Donald Brown

     Rating

    3.00

     Difficulty

    4.00

     GPA

    2.93

     Sections

    Last Taught

    Fall 2014

  • Marc Breton

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2010

  • Frank Deviney

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2009

  • Faculty Staff

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2010

  • Abigail Flower

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2015

  • Jamey Thompson

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Spring 2019

  • Alicia Nobles

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2017

  • Jonathan Hughes

     Rating

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     GPA

     Sections

    Last Taught

    Spring 2020

  • Haroon Lone

     Rating

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     GPA

     Sections

    Last Taught

    Summer 2021

  • Sonia Baee

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2021

  • Seokhyun Chung

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2025