Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis

Statistics    |    Expert
• 15 videos | 2h 11m 29s
• Includes Assessment
Rating 4.8 of 4 users (4)
Two-sample T-tests are great for comparing population means given two samples. However, if the number of samples increases beyond two, we need a much more versatile and powerful technique - analysis of variance (ANOVA). Use this course to learn more about non-parametric tests and the ANOVA analysis. In this course, you'll explore the different use cases for Mann-Whitney U-tests, the use of the non-parametric paired Wilcoxon signed-rank test, and perform pairwise T-tests and ANOVA. You'll also get a chance to try your hand at the non-parametric variant of ANOVA - Kruskal Wallis test and post hoc tests, such as Tukey's honestly significant difference test (HSD). After completing this course, you will be able to account for the effect of one or two independent categorical variables, each having an arbitrary number of levels, on a dependent variable using ANOVA.

WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Recognize the use of the mann-whitney u-test
Use the mann-whitney u-test
Set up data for the paired wilcoxon signed-rank test
Compare the paired t-test and the paired wilcoxon signed-rank test
Identify the pairwise t-test for multiple categories
Use the pairwise t-test to test for different means
Outline the use of one-way anova analysis
• Outline one-way anova and linear regression
Use tukey’s hsd to know which categories differ significantly
Describe how anova requires residuals to be normally distributed
Use the non-parametric kruskal-wallis test
Outline the use of the two-way anova analysis
Use two-way anova with interaction between the independent variables
Summarize the key concepts covered in this course

IN THIS COURSE

• 3.  Comparing Categories with the Mann-Whitney U-test
• 4.  Using the Paired Wilcoxon Signed-rank Test
• 5.  Comparing Paired T-test & Wilcoxon Signed-rank Test
• 6.  Understanding Pairwise T-tests
• 7.  Comparing Values across Groups with Pairwise T-tests
• 8.  Understanding One-way ANOVA
• 9.  Performing One-way ANOVA and Linear Regression
• 10.  Performing the Post-hoc Tukey's HSD Test
• 11.  Checking ANOVA Residuals' Assumptions
• 12.  Using the Kruskal-Wallis Test
• 13.  Understanding Two-way ANOVA
• 14.  Performing Two-way ANOVA with Interaction
• 15.  Course Summary

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