FA.SPID.6
Using technology, create scatterplots and analyze those plots to compare the fit of linear, quadratic, or exponential models to a given data set. Select the appropriate model, fit a function to the data set, and uses the function to solve problems in the context of the data.
Example Problems
An ornithologist wants to look at the relationship between the breadth of peregrine falcon eggs and the mass of the falcon chicks that hatch from them. The data show a linear pattern with the summary statistics shown below:
Find the equation of the least-squares regression line for predicting the chick's mass from the breadth of the egg, in the form .
Round your entries to the nearest hundredth.
| mean | standard deviation | |
|---|---|---|
| x = egg breadth (mm) | ||
| y = chick's mass (g) | ||
Find the equation of the least-squares regression line for predicting the chick's mass from the breadth of the egg, in the form .
Round your entries to the nearest hundredth.
A tutoring center director wants to look at the relationship between hours of tutoring and final grades. The data show a linear pattern with the summary statistics shown below:
Find the equation of the least-squares regression line for predicting the final grade (points) from the hours of tutoring, in the form .
Round your entries to the nearest hundredth.
| mean | standard deviation | |
|---|---|---|
| = hours of tutoring | ||
| = final grade (points) | ||
Find the equation of the least-squares regression line for predicting the final grade (points) from the hours of tutoring, in the form .
Round your entries to the nearest hundredth.
An automotive journalist wants to look at the relationship between engine displacement and fuel economy. The data show a linear pattern with the summary statistics shown below:
Find the equation of the least-squares regression line for predicting the fuel economy (mpg) from the engine displacement (liters), in the form .
Round your entries to the nearest hundredth.
| mean | standard deviation | |
|---|---|---|
| = engine displacement (liters) | ||
| = fuel economy (mpg) | ||
Find the equation of the least-squares regression line for predicting the fuel economy (mpg) from the engine displacement (liters), in the form .
Round your entries to the nearest hundredth.

1-on-1 AI tutoring aligned to FA.SPID.6. Instant help for students, real-time insights for teachers.
Used in classrooms by 100,000+ students at Baltimore County, Plano ISD, Deer Valley USD, KIPP, and districts nationwide.
Free for teachers, forever →