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The Nonparametric Methods topic will provide the participant with the knowledge and skills necessary to employ nonparametric tools and methods to analyze and report on non-normal data. Of interest, this branch of mathematical statistics is concerned with statistical models and tests that are not dependent upon the type or nature of the underlying distribution. Nonparametric models differ from parametric models in that the model is not specified before-the-fact, but is instead determined from data. Nonparametric models are also called distribution free statistics.
This class of statistics is often employed to test the median difference between two or more groups, such as the difference between one vendor and another in terms of a certain performance characteristic. Such methods can also be used to determine if an apparent trend is random in nature or a real trend.
Reinforcement of major concepts, techniques, and application is realized through exercises, scenarios, and case studies. The following prerequisite topics are listed in sequential learning order: Basic Statistics, Hypothesis Testing, Confidence Intervals, Parametric Methods and Chi-Square Methods. Total instructional time for this topic is 1 hours and 20 minutes.
- Nonparametric Concepts - Explain the difference between parametric and nonparametric methods
- Median Test - Execute a median test on two groups and then determine if the difference is statistically significant
- Runs Test - Conduct a runs test to determine if a time series pattern is random
- Other Tests - Identify two nonparametric methods other than a median or runs test