Overview of Nonparametric Data Analysis Techniques
The objective of this course is to acquaint students with the basic ideas, applicability, and methods of nonparametric data analysis. After an introductory overview of fundamental concepts, students will learn ten nonparametric analysis methods.
The course will be laboratory-based. Use of the MINITAB™ statistical software package will be demonstrated using data from case studies. Guidelines for interpreting MINITAB™ output analytically will be presented. Students are encouraged to bring their own data analysis problems to the class for discussion.
· Review of hypothesis testing
· The comfortable cocoon of normality
· Detection of non-normality – emerging from the cozy cocoon
· Nonparametric tests
1 The one-sample sign test
2 The one-sample Wilcoxson test
3 The Mann-Whitney test
4 The Kruskal-Wallis test
6 Cochran’s test for related observations
7 Mood's median test
8 The Friedman test
9 The runs test
10 Squared ranks test for variances
Work experience using basic statistics and/or one basic statistics course (which included the topics of the normal distribution and hypothesis testing) is recommended.
Conover, W. J. 1999. Practical Nonparametric Statistics, 3rd edition. New York, New York: John Wiley & Sons, Incorporated.
Supplemental notes prepared by the instructor.
Access to Web site prepared by the instructor.
Sprent, P., and N. C. Smeeton. 2001. Applied Nonparametric Statistical Methods, 3rd edition. Boca Raton, Florida: Chapman & Hall/CRC.
Edward J. Williams, Adjunct Lecturer of Industrial and Systems Engineering, and of Management and Information Science
This course is intended for analysts, engineers, designers, and managers involved in the collection and interpretation of potentially non-normal observational and/or experimental data.