In a multiple regression problem involving two independent variables, if b1 is computed to be +2.0, it means that

a. the relationship between x1 and y is significant.

b. the estimated average of y increases by 2 units for each increase of 1 unit of x1, holding x2 constant.

c. the estimated average of y is 2 when x1 equals zero.

d. the estimated average of y increases by 2 units for each increase of 1 unit of x1, without regard to x2.

D. the estimated average of Y increases by 2 units for each increase of 1 unit of X1, without regard to X2.

Step-by-step explanation:

Hello!

Given any linear or multiple regression, βi represents the modification on the population mean of Y when Xi increases one unit. And its estimator bi is defined as the modification on the estimated mean of Y when Xi increases one unit.

So if you have an estimated multiple regression model Yi= b₀ + b₁X₁ + b₂X₂ + ... + bkXk

Where k is the number of regressive variables.

b₁ is the modification of the estimated average of Y when X₁ increases one unit and the rest of the independent variables remain constant.

In other words, the estimated mean of Y increases 2 for each unit increase of X₁.

I hope it helps!

step-by-step explanation:

the options are kind of confusing. i can tell you what i see going on here when i look at the graph.

the class and exam medians are closely related, and it looks like the low outlier on the exam did pull the median lower. [tex][/tex]