Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis
Statistics
| Expert
- 15 videos | 2h 11m 29s
- Includes Assessment
- Earns a Badge
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
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Discover the key concepts covered in this courseRecognize the use of the mann-whitney u-testUse the mann-whitney u-testSet up data for the paired wilcoxon signed-rank testCompare the paired t-test and the paired wilcoxon signed-rank testIdentify the pairwise t-test for multiple categoriesUse the pairwise t-test to test for different meansOutline the use of one-way anova analysis
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Outline one-way anova and linear regressionUse tukey’s hsd to know which categories differ significantlyDescribe how anova requires residuals to be normally distributedUse the non-parametric kruskal-wallis testOutline the use of the two-way anova analysisUse two-way anova with interaction between the independent variablesSummarize the key concepts covered in this course
IN THIS COURSE
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1m 49s
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9m 39s
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7m 54s
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9m 43s
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10m 35s
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8m 26s
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9m 56s
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11m 6s
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11m 18s
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11m 14s
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9m 48s
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9m 28s
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9m 48s
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7m 36s
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3m 8s
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