Open your copy of the State Demographics Starter File.

We started with a model based on Alabama and Alaska. We can interpret the slope and 𝑅2 value below:

The Alabama-Alaskasensible name model predicts that a 1 percent[x-axis units] increase in percent college degrees[x-axis] is associated with a 5613 dollar[slope, y-units] increaseincrease / decrease in median household income[y-axis]. Based on the 𝑅2 of -15.63R2 value, this model fits really, really poorlyreally well, decently, poorly, etc..

1 Describe the optimal model YOU created via linear regression:

The linear-regressionsensible name model predicts that a 1 percent[x-axis units] increase in percent college degrees[x-axis] is associated with a [slope, y-units] increase / decrease in median household income[y-axis]. With an 𝑅2 of R2 value, this model fits really well, decently, poorly, etc..

2 What does the slope of this linear model tell us?

3 What does the y-intercept of this linear model tell us?

4 Suppose a state goes from 10% to 11% college graduation. According to this model, what kind of change would we expect to see in the median household income? What if it goes from 50% to 51%? From 90% to 91%?

5 Does this model predict the same increase in income for every additional 1% college-or-higher? Why or why not?

6 Use fit-model to fit your model to the scatter plot again, but swap the x- and y-columns. Do you get the same 𝑅2? Why or why not?

7 Describe another model you created:

The sensible name model predicts that a 1 [x-axis units] increase in [x-axis] is associated with a [slope, y-units] increase / decrease in [y-axis]. With an 𝑅2 of R2 value, model fits really well, decently, poorly, etc..

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