The points on a scatterplot increase rapidly. You run a linear regression on the data. The residual plot shows a pattern, and is low.

Which of these methods would likely result in a better model of the data?

A. Run a quadratic regression on the data and then linearize the data.

B. Linearize the data by taking the log of the response variable, and

then run a linear regression.

C. Linearize the data by taking the logs of both variables, and then

run a linear regression.

D. Linearize the data by taking the log of the response variable, and

then run an exponential regression.

B

Step-by-step explanation:

The method that would most likely result in a better model of the data is to :

Linearize the data by taking the log  of the response variable, and then run a linear regression.

A linear regression can be expressed as

The linear regression is applied  to improve the model fitting and also helping to linearize the responses which causes an increase in the R-squared value.

3/6   and 9/18

step-by-step explanation:

-3

step-by-step explanation:

a drawing really does here:

so i draw the horizontal line y=2 and the vertical line x=-2 at this intersection i put b (this is where my right angle is going to be).   so point b is at (-2,2).   point a is 5 units long and is also on y=2 so this point has to be (3,2) or (-7,2) -doesn't matter i will go with (3,2).

now we know c is at (-2,y) we don't know the y yet bet we know that c is on x=-2.

we have the area of the triangle is .5bh=12.5

we know the base length is 5 (ab)

we have the height is (2-y) if you look at your

plug in

.5(5)(2-y)=12.5

solve for y to get the y-coordinate for c

2.5(2-y)=12.5

divide both sides by 2.5 giving:

2-y=5

now subtract 2 on both sides

-y=3

y=-3