518—Nonparametric Statistical Methods (3) (Prereq: A grade of C or better in STAT 515, STAT 509, STAT 512, or equivalent) Applications and principles of nonparametric statistics. Classical rank-based methods and selected categorical data analysis and modern nonparametric methods. Statistical packages such as R.
Usually Offered: Summer I and Alternating Fall Semesters
Purpose: To introduce the principles and applications of commonly used nonparametric methods. To compare these methods to their parametric counterparts through simulation studies. To introduce the basic methods for analyzing contingency tables.
Current Textbook: Practical Nonparametric Statistics, (3rd Edition) by W.J. Conover, Wiley, 1999.
|Probability and Statistical Inference – probability and counting rules, discrete random variables, continuous random variables, use of SAS and R, properties of estimators, properties of hypothesis tests||
|Tests Based on the Binomial Distribution – the binomial and quantile test, the sign test, McNemar's test||
|Methods Based on Ranks – Mann-Whitney test, Kruskal-Wallis test, squared rank test, measures of rank correlation, nonparametric linear regression, Wilcoxon signed ranks test, Friedman test||
|Goodness of Fit Tests – Kolmogorov goodness of fit test, Kolmogorov test for two samples||
|Categorical Data – chi-squared goodness of fit, chi-square test for r by c contingency tables, Mantel-Haenszel test, Cochran's test for related observations, measures of dependence, loglinear models||
The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.
Contact Faculty: Brian Habing