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Mar 14, 2026
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ACS 3420 - Linear Models and Predictive Analytics3 lecture hours 0 lab hours 3 credits
Course Description
This course is designed to provide students with a solid statistical foundation. It covers the core theory and practical applications of linear models and fundamental predictive analytics techniques. Topics covered include generalized linear models (GLMs), data visualization, principal component analysis (PCA) for reducing dimensions, penalized regression, and nonlinear regression.
Prereq: ACS 3410 or MTH 3410
Note: None
This course meets the following Raider Core CLO Requirement: Integrate Learning
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Perform exploratory data analysis (EDA) including data visualization
- Choose appropriate linear models such as generalized linear models (GLMs) and generalized additive models (GAMs) for data analysis
- Apply Principal Component Analysis (PCA) for dimension reduction
- Use statistical software to solve and model problems in topics covered in this course
- Apply fundamental predictive analytics techniques to address real-world actuarial and business problems
- Interpret and effectively communicate the results for various linear models and predictive analytics methods.
Prerequisites by Topic
- Probability theory and application
- Hypothesis test
- Regression analysis
Course Topics
- Explanatory data analysis
- Generalized linear models (GLMs)
- Generalized additive models (GAMs)
- Principle component analysis (PCA)
- Penalized regression
- Nonlinear regression
Coordinator
Dr. Won Chul Song
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