Use the predictor function and r-value from each linear regression finding on the left to fill in the blanks of the corresponding description on the right.

1

 
 sugar(m) = −3.19m + 12
 r = −0.05

For every additional Marvel Universe movie released each year, the average person is predicted to consume [amount] [more / fewer] pounds of sugar! This correlation is [strong, moderate, weak, practically non-existent].

2

 
height(s) = 1.65s + 52
r = 0.89

Shoe size and height are [strongly, moderately, weakly, not], [positively / negatively] correlated. If person A is one size bigger than person B, we predict that they will be roughly [amount] inches taller than person B as well.

3

 
babies(u) = 0.012u + 7.8
r = 0.01

There is [a strong, a moderate, almost no] relationship found between the number of Uber drivers in a city and the number of babies born each year.

4

 
score(w) = -15.3w + 1150
r = −0.65

The correlation between weeks-of-school-missed and SAT score is [strong, moderate, weak, practically non-existent] and [positive / negative]. For every week a student misses, we predict a [amount] point [gain / drop] in their SAT score.

5

 
weight(n) = 1.6n + 160
r = 0.12

There is a [strong, moderate, weak, practically non-existent], [positive / negative] correlation between the number of streaming video services someone has, and how much they weigh. For each service, we expect them to be roughly [amount] pounds heavier.

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