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Nov 23, 2024
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MAT 302 - Nonparametric Statistics Basic principles of nonparametric methods in statistics including one-, two-, and k-sample location methods. Topics include analysis of variance, correlation, sample comparison, and testing for normality and randomness in samples. Prerequisite: A grade of C- or higher in MAT 201, COM 230, PSY 201, or ECO 221. Fulfills: LASR. (3 cr. hr.)
Frequency code O = offered occasionally
Student Learning Outcomes
Upon successful completion of this course, students will be able to:
- Evaluate the strengths and weaknesses present in various sampling strategies.
- Analyze data in which traditional statistical assumptions may not be applicable.
- Explore nonparametric sampling distributions, using both mathematical probability and simulation-based inferential models.
- Apply nonparametric techniques to semi-structured data.
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