. Statisticians use linear regression to a) Make sure the X and Y scores match b) Predict unknown Y scores from unknown X scores c) Predict known Y scores from known X scores d) Predict unknown Y scores from known X scores

D

Step-by-step explanation:

In this question we are asked to pick the correct option that clearly states what statisticians use linear regression for.

To answer this question, I shall be considering the validity of the options one after the other.

But before that , what is a linear regression model?

A linear regression model employs a linear plot to explain the relationship between a dependent variable and another variable called the independent variable.

Option a is wrong.

A regression model does not say if the dependent and independent variables match

Option B is wrong

A linear regression model cannot be used to predict the value of an unknown dependent variable from the unknown Independent variables. X is the independent here with y the dependent

Option C is wrong

Since Y scores is known, then what are we predicting again?

Option. D is correct

We predict values of the unknown dependent variable y from the known independent variable x

option 3 . (5x + 1) - (-10x + 6)

step-by-step explanation:

option 1 :

(4x + 3) + (-9x + 4)

or 4x+3 -9x + 4

or -5x + 7

option 2

(x - 2) - (6x - 9)

or x-2-6x + 9

or -5x + 7

option 3

(5x+ + 6)

or 5x + 1 + 10x -6

or 15x -5

option 4

-4x+2x+7-3x

-7x + 2x + 7

-5x + 7

hence only option 3 did   not satisfy the given simplify expression.