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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.

Prerequisites

Special information

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

Effective May 9, 2011 to present

Learning outcomes

General

  • Summarize the analysis of statistical models when applied to specified data sets.
  • Select among different nonparametric models for hypothesis testing, including assumptions about model data.
  • Develop statistical models and hypothesis testing techniques such Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit-tests, nonparametric linear correlation and regression, and nonparametric methods for paired data.
  • Apply statistical principles and methods for nonparametric statistical methods.