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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
Testing for Normality
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This lesson is called Testing for Normality, part of the Inferential Statistics with R course. This lesson is called Testing for Normality, part of the Inferential Statistics with R course.
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# Density plot # college %>% ggplot(aes(age)) + geom_density() # q-q plot # college %>% ggqqplot("age") # Shapiro-Wilks test # college %>% shapiro_test(age)
Using each of the three methods, test whether
age is normally distributed. Come to a conclusion: is age normally distributed?
Read more about testing for normality in R.