Correct answer
Because it ensures that the variance of the
error terms is the same across all levels of the 1.5 / 1.5 pts
C predictor. You selected this answer. This was
[——— the correct answer.
Why is the constant variance assumption important in regression?
o Because it ensures that the variance of the error terms is the same across all levels of the
predictor
Because it guarantees that the predictor variables are independent
Because it ensures the response variable is normally distributed.
Because it makes the regression coefficient unbiased
Module 1, Lesson 1.2
The constant variance assumption ensures equal accuracy across the data
range. If violated, predictions and intervals become poorly calibrated.
,Question 2 1.5/ 1.5 pts
In simple linear regression with Y the response and X the predictor, what does 31
repl The standard deviation of residuals..
The expected value of Y when X=0
o The expected change in'Y for a one-unit increase in X
The standard deviation of residuals.
The intercept of the regression line.
Module 1, Lesson 1.2
(1 is the slope, interpreted as the change in response for a one-unit change in
predictor.
, Question 3 0/ 1.5pts
In multiple linear regression with an intercept, the sampling distribution of &2 follows
a with degree of freedom, where n is the number of observations
and p is the number of model predictors.
Note: The number of model predictors (p) does not include the intercept.
Select the most appropriate answer below to fill in the blanks.
T-distribution; n-p
T-distribution; n-p-1
@ chi-squared distribution; n-p
-> chi-squared distribution; n-p-1
Lesson 2.2
Assuming that the error terms are normally distributed, the sampling
distribution of the estimated variance, or so-called mean squared errors or
MSE, is a chi-squared distribution with n-p-1 degrees of freedom.
Because it ensures that the variance of the
error terms is the same across all levels of the 1.5 / 1.5 pts
C predictor. You selected this answer. This was
[——— the correct answer.
Why is the constant variance assumption important in regression?
o Because it ensures that the variance of the error terms is the same across all levels of the
predictor
Because it guarantees that the predictor variables are independent
Because it ensures the response variable is normally distributed.
Because it makes the regression coefficient unbiased
Module 1, Lesson 1.2
The constant variance assumption ensures equal accuracy across the data
range. If violated, predictions and intervals become poorly calibrated.
,Question 2 1.5/ 1.5 pts
In simple linear regression with Y the response and X the predictor, what does 31
repl The standard deviation of residuals..
The expected value of Y when X=0
o The expected change in'Y for a one-unit increase in X
The standard deviation of residuals.
The intercept of the regression line.
Module 1, Lesson 1.2
(1 is the slope, interpreted as the change in response for a one-unit change in
predictor.
, Question 3 0/ 1.5pts
In multiple linear regression with an intercept, the sampling distribution of &2 follows
a with degree of freedom, where n is the number of observations
and p is the number of model predictors.
Note: The number of model predictors (p) does not include the intercept.
Select the most appropriate answer below to fill in the blanks.
T-distribution; n-p
T-distribution; n-p-1
@ chi-squared distribution; n-p
-> chi-squared distribution; n-p-1
Lesson 2.2
Assuming that the error terms are normally distributed, the sampling
distribution of the estimated variance, or so-called mean squared errors or
MSE, is a chi-squared distribution with n-p-1 degrees of freedom.
