This module offers an overview of nonparametric statistics. It aims to familiarise students with a broad range of topics. Emphasising practical application, the module covers various topics including levels of measurement, comparisons between two independent or dependent populations, goodness-of-fit tests, nonparametric analysis of variance, and correlation. It also coves the estimation of distribution functions, bootstrap methods, kernel density estimation, and nonparametric regression.
Learning Outcomes
By the end of the module students should be able to:
Identify when to use a nonparametric method.
Apply and implement different nonparametric methods in estimation, testing, model fitting, and in analyses.
Formulate, test and interpret various hypothesis tests for location, scale, and independence Problems.
Use statistical methods, including nonparametric bootstrapping, to construct and interpret
interval estimators for population medians and other population parameters based on rank-based methods.
Produce and interpret statistics and graphs, using nonparametric density estimation.