Mastering Normality Tests in R: Graphical & Statistical Methods | Statistics with R
Welcome to our comprehensive tutorial on testing for normality using R programming. In this video, we'll explore both graphical and statistical methods to determine if your data adheres to a normal distribution, which is crucial for applying parametric tests like t-tests and ANOVA. We'll cover popular tests like Shapiro-Wilk and Kolmogorov-Smirnov, and explain when and how to use them effectively with practical examples using the `women` dataset in R.
What you will learn:
Graphical methods for normality testing: Histograms, QQ plots, and more.
Statistical tests: Shapiro-Wilk Test, Kolmogorov-Smirnov Test.
How to interpret the results from these tests.
Applying these tests to the `women` dataset.
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00:00 - Introduction to Normality Testing in R
00:04 - Importance of Distribution Testing in Statistics
00:26 - Graphical Methods for Assessing Normality
01:27 - Introduction to Statistical Normality Tests
07:10 - Creating QQ Plots with ggplot2
07:26 - Shapiro-Wilk Test Explained
09:16 - Kolmogorov-Smirnov Test Explained
13:16 - Final Thoughts and Recommendations
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