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STAT 331 Nonparametric Statistical Methods

This course covers the fundamental to intermediate ideas of nonparametric statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include nonparametric methods for paired data, Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit tests, nonparametric linear correlation and regression. Completion of STAT201 (Statistics I) is a prerequisite for this course.


Special information

Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at
4 Undergraduate credits

Effective May 9, 2011 to present

Learning outcomes


  • Communicate understanding of analysis results through clearly written conclusions summarizing the results of the statistical models when applied to specified data sets.
  • Demonstrate the ability to appropriately select among different nonparametric models for hypothesis testing, including assumptions about model data, in the context of answering questions about representative real-world problems.
  • Understand and learn to interpret a more general set of statistical models and hypothesis testing techniques (not covered in STAT 201 and built on understanding of hypothesis testing) such Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit-tests, nonparametric linear correlation and regression, and nonparametric methods for paired data.
  • Understand statistical principles and methods for nonparametric statistical methods.