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Home  /  Six Sigma Study Guide Articles  /  Performing Hypothesis Tests with Non Normal Data

Category: Performing Hypothesis Tests with Non Normal Data

Ted Hessing

Chi-Square Test of Independence

Posted by Ted Hessing

Learn how to use the Chi-Square Test of Independence in Six Sigma to detect relationships between categorical variables. Step-by-step guide.

This entry was posted in Analyze, Performing Hypothesis Tests with Non Normal Data and tagged ASQ, Black Belt, Green Belt, IASSC, Villanova. Bookmark the permalink.
Ted Hessing

What is the Coefficient of Contingency?

Posted by Ted Hessing

Understand what the Coefficient of Contingency is and how it measures the strength of relationships between categorical variables.

This entry was posted in Analyze, Performing Hypothesis Tests with Non Normal Data and tagged ASQ, Black Belt, IASSC, Villanova. Bookmark the permalink.
Ted Hessing

1 Sample Wilcoxon Non Parametric Hypothesis Test

Posted by Ted Hessing

1 sample Wilcoxon Non Parametric Hypothesis Test is a rank-based test and it compares the standard value with a hypothesized median.

This entry was posted in Analyze, Performing Hypothesis Tests with Non Normal Data and tagged ASQ, Black Belt, IASSC. Bookmark the permalink.
Ted Hessing

1 Sample Sign Non Parametric Hypothesis Test

Posted by Ted Hessing

The 1 sample sign non parametric hypothesis test simply computes a significance test of a hypothesized median value for a single data set.

This entry was posted in Analyze, Performing Hypothesis Tests with Non Normal Data and tagged ASQ, Black Belt, IASSC, non-parametric. Bookmark the permalink.
Ted Hessing

Friedman Non Parametric Hypothesis Test

Posted by Ted Hessing

The Friedman non parametric hypothesis test is an alternative to the one-way ANOVA with repeated measures.

This entry was posted in Analyze, Performing Hypothesis Tests with Non Normal Data and tagged ASQ, Black Belt, IASSC, non-parametric, QuestionSet. Bookmark the permalink.
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