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NPTEL

Basics of Nonparametric Tests with Applications by R

NPTEL via Swayam

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Overview

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ABOUT THE COURSE:Nonparametric inference is essential when either the usual Gaussian assumption would fall short or sample size is too small to undergo with the Gaussian assumption. Till date most of the data analysis adheres to the test constructed on Gaussian assumption. This course will enable the students to understand why and when nonparametric tests are more pertinent. The theme of the course is not only focusing towards theory but also emphasizing more to data analysis.Although this course is designed for a 4-week (10 hr) course the learning items under it covers almost all basic nonparametric tests valid for potential Analysis. Students will learn one sample location and scale test, two sample tests, K-sample tests like nonparametric ANOVA and goodness of fit tests, garnered with evoking examples. The course will benefit not only the undergraduate students of Statistics but also guide the researchers from different disciplines like biological sciences, social sciences, agricultural sciences.INTENDED AUDIENCE: Undergraduate students in Statistics/Data Sciences, Researchers from BioSciences, Agriculture Sciences, Social Sciences etc.PREREQUISITES: A minor course in Introductory Statistical InferenceINDUSTRY SUPPORT:Business Analytics industryBanking SectorNSSO/CSO

Syllabus

Week 1:Why Nonparametrics?
Need of nonparametric inference, concept of rank, permutation, inversion, Kendall’s tau and Spearman rank correlation, linear rank statistics
Week 2:One sample testing problem
Test of location parameter: sign test, Wilcoxon signed rank test, paired observations, power analysis, Test on scale parameter:Mood Test, Problem Solving using R
Week 3:Two-sample testing problem
Mann Whitney U test,, Signed rank test, Two sample Mood test, Run test, Extension to K sample test: Kruskal Wallis and Friedmann Test
Week 4:Goodness of Fit Test
Pearson Chi-square test, one sample K-S test, Anderson Darling Test, Two sample K-S test, Solving example by R

Taught by

Prof. Saran Ishika Maiti

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