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Spring 2023
Mar 01, 2024
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Information Select the desired Level or Schedule Type to find available classes for the course.

MATH 454 - Statistics for Risk Modeling
Covers analytic methods for risk modeling including statistical learning, generalized linear models, time series models, principal components analysis, decision trees, and cluster analysis. These analytic methods will be applied to examples using the R statistical computing language. Prerequisite: C- or better in MATH 321 or permission of the department chairperson.
4.000 Credit hours
4.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture, Immersive Learning, Undergraduate Research, Study Abroad, Societal Issue/Global Chal

Mathematical Sciences Department

Restrictions:
Must be enrolled in one of the following Levels:     
      Undergraduate
      Graduate

Prerequisites:
Undergraduate level MATH 321 Minimum Grade of C-

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