Courses :: Module, NONPARAMETRIC STATISTICS

	Overview
	Checking distributional assumptions. Consequences of non-normality for t-and F-tests. Ranks and order statistics. Sign test and Wilcoxon signed-rank test for single samples. The Wilcoxon-Mann-Whitney test for two-samples. The Kruskal-Wallis test for k-samples. Association and correlation. Bootstrap methods. Density estimation. Non-parametric smoothing.

Overview

Checking distributional assumptions. Consequences of non-normality for t-and F-tests. Ranks and order statistics. Sign test and Wilcoxon signed-rank test for single samples. The Wilcoxon-Mann-Whitney test for two-samples. The Kruskal-Wallis test for k-samples. Association and correlation. Bootstrap methods. Density estimation. Non-parametric smoothing.

	Learning Outcomes
	On successful completion of the module, students should be able to: Understand a wide range of non-parametric tests and identify when they are appropriate to use. Implement non-parametric methods using software such as R. Fit non-parametric regression curves using various algorithms.

Learning Outcomes

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? Yes University scheduled written examination (Autumn): 120 minutes, On-campus

Autumn Supplementals/Resits

	Pre-Requisites
	ST201

Pre-Requisites

	Timetable
	Click on the link to open the Timetable for Module [ST204]. Venue locations can be found here.

Timetable

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NONPARAMETRIC STATISTICS