Non Parametric Statistics
Non-parametric statistics is part of inferential statistics that is divided into the following eight major topics: sign tests, signed rank tests, rank-sum tests, and Friedman analysis of variance by ranks, runs tests, rank correlation methods, Chi-square tests and Kolmogrov –Smirnov test. The estimation and hypothesis testing for non-normal populations.
By the end of this course students should be able
- Distinguish between parametric and non-parametric methods.
- Carry out different tests of hypothesis using different non- parametric methods.
- Choose appropriate methods and tests/statistics for given problem.
- Produce and interpret statistics and graphs, using nonparametric density estimation and non-parametric function estimation techniques.
- Present and communicate, both orally and in written-form, the results of statistical analyses of nonparametric data.