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.

  21.3

step-by-step explanation:

multiplying the given equation by c/sin(25°), we get

  c = 9/sin(25°) ≈ 21.2958 ≈ 21.3

we assume "c" is the length of ab, the side opposite angle c in triangle abc.

answer: i realy don't get it that much can you me figer it out pleas you

step-by-step explanation: