Open your copy of State Demographics Starter File. If you haven’t already, Save a Copy now.
We’ve already built models for pct-college-or-higher and median-income, but that’s not the only relationship in this dataset!
1 What other relationship are you curious about?
explanatory variable (x) and response variable (y)
2 Fill in the code to make a scatter plot exploring the relationship between those columns:
scatter-plot(states-table, "state", , )
3 Pick two states that you think would generate a good first model: and
4 Calculate the slope and y-intercept for a line passing through these two point and use them to define your model. Then type this model into Pyret, and fit it to your data use fit-model.
fun my-new(x): ( * x) + end S-value
5 Use lr-plot to build the optimal linear model for this relationship.
fun best-new(x): ( * x) + end S-value
6 Based on these S-values, we would expect predictions made with this best possible model to have percent less error than predictions made with the linear model I generated from two points.
7 What does this model actually mean? Try completing the sentences below:
This model predicts that a 1
percentx-axis units increase in
explanatory variable (x) is associated with a
rate of change in y-units
increaseincrease / decrease in
response variable (y).
Based on the S of
S and
response variable (y) ranging from
lowest y-value to
highest y-value,
I
strongly agree, agree, disagree, strongly disagree that this model is a good fit.
These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). 
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