 Perhaps the best way to look at the association between two quantitative variables is with scatter plots or the variations on scatter plots. And raw gives us several options or variations on scatter plots. We'll begin with an easy one, the regular scatter plot, which is right over here, and they call it scatter plot two words. I'm going to click on that. And then for this, I'm going to use a different data set, I'm going to come up and change to the movies data set, because it has several quantitative variables in it. Even though it's a relatively small number of records, only 26. But let's come down here to the bottom and see what our options are. We actually have five different things that we can put in it, we won't necessarily use all of them. The most important are the X and the Y axis going across and going up. Let's begin by taking the ratings of a movie from the internet movie database IMDB, let's put that on the X axis. And when I do that, you can see that we got this axis that goes from 4.5 to nine to cover the observed range of ratings in this data set. And then I'm going to take the total box office that is how much money did the movie make when it was in theaters and put that in the Y. And now we have a basic scatter plot right here. And you can see that it's basically uphill, which means that in general, the higher the rating of the movie on IMDB, the more money it made, there are exceptions, but it's the general trend that we can get a little more insight into this if we throw in a few other variables. So for instance, I'm going to take the amount of money that was invested in the movie through the production budget. And I'll put that in here for size. And you can see that some of these cost a lot more than others, which are much smaller. And so that's an important piece of information and trying to understand the patterns here. But another one is that we have the genre of the movies that might make a big difference, especially when you think about the summer action blockbusters. So I'm going to put that right here under color. And now you can see that for instance, this reddish color, that's action movies, and those are some of the highest rated and highest earning adventure, which is closely related here in the green, and so on. And then the last thing I want to do is we actually want to see what these movies are. So I'm going to take the movie title, which is just called movie, it's a string or character variable, and bring that here under labels. And now we can see that avatar got an average of eight on IMDB and brought in over $700 million in the box office, at least when this is reported, that if all the movies in this 26 row data set, the Dark Knight had the highest rating here we have Star Wars and I can't believe King Kong is even in this list. And it confirms my suspicions that Twilight has the lowest rating of all of them here. Anyhow, this is a neat way of showing several variables. We have the IMDB ratings, we have the earnings, we have the production cost, we have the genre, all of those in a single visualization, which make a scatterplot with the modifications available on raw, a great way of looking at the associations between several different variables.