1 Below we see the model lizard-adopt-time(pounds) = (-0.5 * pounds) + 9 fit to the lizard sample.

a scatter plot displaying pounds v weeks and the linear model lizard-adopt-time(pounds) = -0.5 * pounds + 9 diagonal. We also see S:~1.56 R²:~0.29

This model predicts that lizards weighing 0 pounds will be adopted in y-intercept y-units, and that,
for every additional x-variable units, x-variable will increase/decrease by rate of change x-units.

The error in the model is described by an S-value of about S units. I strongly agree, agree, disagree, strongly disagree that this model is a good fit considering that y-variable units in this dataset range from lowest y-value to highest y-value.

2 When we mouse over a fit-model visualization, one of the popup boxes we’ll see is titled "Residuals".
Residuals:

x: 34.5

y: 25000

y₁: 45000

What can we learn from this set of values?

3 A government office reported that the error in a model they made is described by an S-value of 3000. Is the model a good or bad fit? Explain.

4 In order to interpet an S-value we need to know:


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