Intro:Non-Parametric Stats

3 Credits · Level: Graduate, Undergraduate

Prerequisite: MATH 5151 . This MS level course provides a survey of nonparametric and rank based methods for data analysis. Frequently, common assumptions about the distributional form of data, required for many hypothesis testing or model building techniques, are violated. Ignoring these violations can lead to misleading or incorrect conclusions. Nonparametric and rank based methods provide statistically valid methods of analysis while requiring minimal assumptions about the data to be analyzed. Some topics covered will include one, two, and K-sample tests of hypotheses including, but not limited to, permutation tests, Wilcoxon Rank-Sum test, Mann-Whitney test, and the Kruskal Wallis test. Further topics including paired comparisons, blocked design, multivariate tests, and nonparametric bootstrap methods, among others, will also be covered. Students should expect a mix of theory and application.