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Inferential Statistics with R
- Welcome to Inferential Statistics with R
- Introduction to the Dataset
- Independent t-test
- Dependent t-test
- One-Way ANOVA
- Post Hoc Comparisons
- Other ANOVA Tests
- Dealing with Small Cells
- Linear Regression
- Multiple Regression
- Hierarchical Regression
- Extracting Output
- Reporting Results
- Testing Assumptions
- Testing for Normality
- Testing for Homogeneity of Variance
- Violated Assumptions
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This lesson is called Violated Assumptions, part of the Inferential Statistics with R course. This lesson is called Violated Assumptions, part of the Inferential Statistics with R course.
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Try the various transformations on the
age variable. Do any of them improve the normality of the variable? Test using the
shapiro_test() function as you learned in the “Testing assumptions” lesson.
Read more about the Winsorize() function in the DescTools package.
Read more about applying transformations in R.
Read more about performing the non-parametric equivalent statistics in the rstatix package:
wilcox_test() as the non-parametric equivalent of the independent samples t-test (note: this is synonymous of the Mann-Whitney U test)
sign_test() as the non-parametric equivalent of the dependent samples t-test
kruskall_test() as the non-parametric equivalent of the one-way ANOVA
friedman_test() as the non-parametric equivalent of the one-way repeated measures ANOVA