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In the last pair of movies we've looked at the variations on the bar chart that let you use two categorical variables to predict scores on a third categorical variable or on a scale variable. In this movie, we'll change the balance a little by looking at a chart for times when you have two scaled variables and one category. This calls for a simple variation on the scatter plot that we covered in this section on bivariate graphs. The only big difference is that we'll be adding group markers for the categorical variable. In this example, I'll use the Google Search's information from Searches.sav.
To get this, I need to go over to Graphs to Chart Builder. From there, I go down to the bottom- left on gallery and I go to Scatter. Now I wanted to use the second one on the top, which is called a Group Scatter, and I drag that out to the canvas. From there, I need to get my predictor variable. Let's scale my predictor variable that's the category and my outcome variable that's a scaled variable. For this example, I am going to use interest in the NBA as a search term. So I am going to come over here and get NBA as a Google search term. I am going to drag that over to the Y-axis.
Then I am going to use two predictors. One is I am going to use the median age of people who live in the state. That median age. That's a scaled variable. so I am going to put it in the X-axis and then it makes sense to me that interest in the NBA would be related to whether a state has an NBA team. So I am going to get has NBA that as a 01 indicator variable and drag that over to set color. Finally, as I scatter plot, you can sometimes find unusual points and you want to see who they are.
So I am going to come down to the tab for groups and points ID. There I am going to click on point ID label at the bottom. Back on the canvas is add the box for point label variable, and I am going to use this state code. So I'll just drag that over and now I am ready to go. Press OK and I get a slightly complicated chart because of all the data names. I am going to edit those out for a moment, but because I've used a variable, I'll be able to bring some of them back if I want. So I'll double click on it.
I can just select the names and I hit Delete for right now. So what I have is a bunch of blue circles and a bunch of green circles. The blue circles are for states that do not have NBA teams. The green circles are for states that do. To make these little bit easier I am going to modify them and make them solid. I'll just click on one. It looks like I better click again to get just the green ones and click on Fill and make that the same shade, green, and that actually has an effect of making all of them solid.
Now what I can do is I can click on regression lines. up on the menu bar here at the second option is called Add fit line at subgroups, as for regression line, separately for each group. I can click on that and I get two lines. One in green for the states that do have NBA teams and one in blue for the states that don't. I also see that we have an outlier and what I am going to do is I am going to come over to the left of this bar to the little target thing. There's the data label mode.
I can click on that and now because earlier I said that I was going to use the state abbreviations as data labels, I can come right down here, click on this outlier, and I can see that it's Utah. Now there's something then. The Utah Jazz seems to elicit unusual levels of fan support. Also people in Utah tend to be rather young on average. I am going to close this chart because I am done editing it, and now I can see that there is an association between age and whether a state has an NBA team that can predict their level of interest in NBA as a search term.
Just as we saw with bivariate graphs, scatter plots are great way to show the relationship between two scaled variables, and then by simply changing the markers, you can add a third categorical variable and you can even see how that new variable changed the relationship between the other two.
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