ANOVA in RStudio Part 1 | Visualization, Normality Assumption, Homogeneity of Variance

In these two installments, I demonstrate how to run an #ANOVA test in #RStudio.
Specifically, in the first video, I will discuss:
1 Data visualization
2 Assumption 1. Normality (of residuals)
3 Assumption 2. Homogeneity of variances: Levene's test; Bartlett's test
In the second video, I cover:
4 Factorial #ANOVA: one-way, two-way, three-way ANOVA
5 Model fit: AIC, BIC, AICwt, and BICwt
6 Effect size: (Partial) eta^2, omega^2, epsilon^2
7 Post-hoc analysis: "hsd", "bonferroni", "lsd", "scheffe", "newmankeuls", "duncan"
8 Visualize post-hoc results with confidence intervals
GitHub link for downloading the dataset and #R Scripts
github.com/AriaAvi/Multivaria...
Videos on getting started with R:
• Getting Started with R...
Videos on how to interpret ANOVA and report the results:
• ANOVA 7: How to report...

[@claireluo9173]

Very nicely explained! Thank you!

[@TaraGhimite]

very helpful, thank you Sir

[@ImthiyasMSM]

How can I contact you please

[@guihuajia7696]

I can't find 42# and 43# samples from ANOVA dataset, how to figure out? Thank you.

[@ip7801]

I know this video is old but I was wondering, at the begining, when you are performing the ggplots (arround the 9:20 mark), why do you use a diffferent alpha value for the different plots?