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|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
Schedule Types: Lecture, Immersive Learning, Undergraduate Research, Study Abroad, Societal Issue/Global Chal
Mathematical Sciences Department
Must be enrolled in one of the following Levels:
Undergraduate level MATH 321 Minimum Grade of C-
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