Referenced from lesson Linear Regression
Each description on the left is written about the linear regression findings on the right. Fill in the blanks using the information in the line of best fit and the r-value.
1 |
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, non-existent]. |
y = −3.19x + 12 r = −0.05 |
2 |
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. |
y = 1.65x + 52 r = 0.89 |
3 |
There is [a strong, a moderate, a weak, no] relationship found between the number of Uber drivers in a city and the number of babies born each year. |
y = -15.3x + 1150 r = 0.01 |
4 |
The correlation between weeks-of-school-missed and SAT score is [strong, moderate, weak, non-existent] and [positive / negative]. For every week a student misses, we predict a more than a [amount] point [gain / drop] in their SAT score. |
y = −5.35x − 16 r = −0.65 |
5 |
There is a [strong, moderate, weak, 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. |
y = 1.6x + 160 r = 0.12 |
These materials were developed partly through support of the National Science Foundation,
(awards 1042210, 1535276, 1648684, and 1738598). 
Bootstrap:Data Science by Emmanuel Schanzer, Nancy Pfenning, Emma Youndtsmith, Jennifer Poole, Shriram Krishnamurthi, Joe Politz, Ben Lerner, Flannery Denny, and Dorai Sitaram with help from Eric Allatta and Joy Straub
is licensed under a
Creative Commons 4.0 Unported License.
Based on a work at www.BootstrapWorld.org.
Permissions beyond the scope of this license may be available by contacting
schanzer@BootstrapWorld.org.