ANOVA stands for analysis of variance and it is basically the action of comparing different means of data sets of various groups.

The difference between these means will tell you if the two data sets are significant to each other.

There are multiple ways of finding the ANOVA.

• One type of ANOVA test is a one-way ANOVA test which uses one independent variable. The equation is:

\[ F = \frac{MS_{between}}{MS_{within}} \]

-Another type of an ANOVA test is a two-way ANOVA test which uses two independent variables instead.

The equation is: \[ F_A = \frac{MS_A}{MS_E} \]

Here is an example of a plot with two data sets where we could use ANOVA equations to see similarity.

library(ggplot2)

amount_of_food = rnorm(50)
weight_of_person = rnorm(50)
data = data.frame(amount_of_food,weight_of_person)

Here is another example of a plot that uses ANOVA. We use ANOVA tests in order to see if data sets are related to each other. Looking at this data we can see if the amount of video games played is related to what eye color you have.

Lastly we can look at what role ANOVA tests play in graphs. They are a very important aspect of statistics and we can visualize an example of how they would work in a graph given.