All right. We’re going to show you how to use JASP to conduct a correlation, or to produce the correlation statistic.
This is some data I found. This is if you’re a fan of movies and follow Rotten Tomatoes. Rotten Tomatoes scores movies based on ratings, both the moviegoer and a critic score, but that score is called a Freshness Score. So here I have, let’s see how many. I have 31 movies. And I have a Freshness Score. And then, how much that movie made reported in millions. So the box office receipts, the critic score, the Rotten Tomatoes score called the Freshness Score, and then this is the year the movie was produced, and then the name of the movie. We won’t be using those two columns. But we’re going to see if there’s a correlation, if better rated movies make more money. If there is a significant relationship.
So we go to regression and we ask for a correlation matrix, and we simply plug in the year and the title. And then we can see our matrix here. And the Pearson R, the correlation, is 0.214, and the P value is 0.248. So the P value is above a 0.05. So there doesn’t appear to be a significant relationship between the rating or the quality measure of a movie, and how much that movie makes.
We also want to plug in a plot. So if we go to plot, and there’s our correlation plot, give it a second. And there it is. That is all of the data points plotted, so you can visually see the relationship. Each one of those dots represents a single movie, an intersection between how much money they made, and their Freshness Score on Rotten Tomatoes. So there you go. Correlation in JASP using Rotten Tomatoes Freshness Score, correlated with how much money the movie makes.