Regression - Assumptions

You are required to setup a predictive equation involving variable 1 and variable 2. First you plot the DATA (see below) to determine if the linear regression applies.

DATA

Variable 1

-0.21582 0.56997 -0.54850 -0.12385 0.06975 0.16327 -0.72595 0.22500 -0.40463 0.67652 -0.82322 0.06747 0.74055 -0.71577 -0.82231 -0.47603 0.58094 -0.58573 0.19003 -0.49528 0.93083 0.61389 -0.91742 -0.60957

Variable 2

0.89369 -0.72620 -0.09185 0.50086 -0.73607 0.88498 -0.27512 0.62647 0.92432 0.56368 0.73005 -0.74824 0.79412 -0.04509 -0.70951 0.01573 0.51169 0.10376 -0.90089 0.04767 -0.16886 -0.65529 0.25296 -0.24747

(Answers)

You decide:

a) Linear regression is not applicable because the point pattern is curvilinear (has a curve).

b) You need more information before deciding to use linear regression

c) Linear regression is not useful because the points have no discernable pattern.

d) The linear regression equation will be very useful because the points have a strong linear pattern.

e) Linear regression is not applicable because it appears that there are two linear patterns indicating that the data come from two populations.

variable1 variable2 -0.21582 0.89369 0.56997 -0.72620 -0.54850 -0.09185 -0.12385 0.50086 0.06975 -0.73607 0.16327 0.88498 -0.72595 -0.27512 0.22500 0.62647 -0.40463 0.92432 0.67652 0.56368 -0.82322 0.73005 0.06747 -0.74824 0.74055 0.79412 -0.71577 -0.04509 -0.82231 -0.70951 -0.47603 0.01573 0.58094 0.51169 -0.58573 0.10376 0.19003 -0.90089 0.49528 0.04767 0.93083 -0.16886 0.61389 -0.65529 -0.91742 0.25296 -0.60957 -0.24747