We can see that there's a regression line that's been added, that thin line that's going diagonally through it. So, let's just briefly pause and kind of observe here. And congratulations, we've made our first scatter plot. I'm just gonna go ahead and click OK now. Your audience would be very disoriented by any report where you didn't follow that convention. It's just a rule, it's always done that way. Now, your dependent variable always goes into the Y-axis, and your independent variable always goes into the X-axis. Okay, so I've dragged the scatter-plot symbol up to the canvas, and now I'm gonna choose miles per gallon as my Y, and weight as my X, I'm gonna drag it over here. You can add the fit line in a later step. You may find that that choice is not available to you.
So, we're gonna down to Scatter/Dot, and there's a new feature in version 25, there's a choice, simple scatter plot with fit line. If we return to the chart builder, those same symbols are visible here, and we can start making our scatter plot. Since we don't talk about a model year like 85 1/2, it really should be declared as ordinal, and not as scale. Nominal variables are separate and distinct categories, and ordinal variables like model year are also separate and distinct categories, but where they're meaningfully ranked. A scale variable is like height and weight, where decimal places and so on make sense. The chart builder will automatically adjust the settings based upon these variable types. If you go to the Variable View, you can see where it can be declared. It's terribly important that those are declared properly. We have miles per gallon, it has a ruler next to it. See the symbols next to the variables here.
It reads, "Before you use this dialog, "measurement level should be set properly "for each variable in your chart." Let me check off "Don't show this dialog again," click on OK, and I'm gonna briefly cancel out of Chart Builder, and walk you through what it's talking about. Let's briefly talk about this warning message, and then I'm gonna choose the selection to not show this again. I'm gonna recommend Chart Builder, and that's what we're gonna use. SPSS is a large, complicated software, so there's often a lot of options for doing the same thing. And I'm gonna go ahead and choose weight as my single independent variable. What we're gonna do is pretend that our focus at the moment is predicting miles per gallon, so that will be our dependent variable, using one of the other variables. We see on the far right-hand side we have the name of the vehicle, and we've got miles per gallon, cylinders, displacement, and several others. I've just made a couple of minor modifications to it, and that's what we're gonna be working on this scatter plot. Auto MPG is a modified version of a file that I got from the well-known and very useful UCI data repository. Okay, so the first thing we're gonna do is go to the data window. IBM started offering the subscription with version 25, but everything I'm gonna be showing you would apply with any recent version. And you'll notice as it's loading, that it says IBM SPSS Statistics Subscription. So, in our resources folder, there is a file called Auto MPG Modified, and we can just simply double-click on that, and that's gonna launch SPSS.
Our broader subject is simple linear regression, which is the prediction of one scale variable with one other variable, and there's no better way to do that than scatter plots. Okay, let's get started by talking about scatter plots.
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