Consider the following statements: I. Multicollinearity is present when there is a high degree of linear correlation between the predictors.
II. A regression analysis between weight (y in pounds) and height (x in inches) resulted in the following least squares line: y-hat = 135 + 6x + errors. This implies that if the height is zero, the weight is 135 pounds in this linear model.
a. I is true and II is false.
b. I is false and II is true.
c. Both I and II are true.
d. Both I and II are false.
e. More information is needed for each statement in order to tell which is true or false.

d. Both I and II are false

Step-by-step explanation:

When there is a high degree of linear correlation between the predictors the errors are found.

The basic objective of the regression model is to separate the dependent and independent variables. So if the variables have high degree of linear correlation then the multi collinearity causes problems or has errors. It is not necessary that multi collinearity must be present with high degree of linear correlation.

For example we have 3 variable of heat length and time. And all of them have a high degree of correlation. With increase in heat and time the length increases . But for multi collinearity with the increase of time and decrease of heat length does not increase. So this causes errors.

y-hat = 135 + 6x + errors

The linear relationship between height and weight is inexact. The deterministic relation in such cases is then modified to allow the inexact relationship between variables and a non deterministic or probabilistic model is obtained which has  error which are unknown random errors.

y- hat= a + bXi + ei   (i=1,2,3...)

ei are the unknown random errors.

So both statements are false.

f

step-by-step explanation:

f