I'm trying to better understand linear regression by the following questions.
(1) Linear regression assumes:
a. The relationship between X and Y is a straight line.
b. The residuals are normally distributed.
c. The residuals are homoscedastic.
d. Both homoscedastic and normally distributed residuals.
(2) Often times, residual plots as well as other plots of the data will suggest some difficulties or abnormalities in the data. Which of the following statements are not considered difficulties?
a. A nonlinear relationship between X and Y is appropriate.
b. The variance of the error term (and of Y) is constant.
c. The error term does not have a normal distribution.
d. The selected model fits the data well except for very few discrepant or outlying data values, which may have greatly influenced the choice of the regression line.
(3) The Analysis of Variance (ANOVA) table in linear regression can be used to compute:
a. R-Squared
b. Adjusted R-Squared
c. The Overall F statistic
d. R-Squared, Adjusted R-Squared, and the Overall F statistic
(4) Consider a linear regression model with the predictor variables X1, X2, and X3. If we regress X1 on the other two predictor variables X2 and X3 and get an R-Squared value of 0.25, then the corresponding Variance Inflation Factor (VIF) for X1 is:
a. 0.25
b. 0.50
c. 0.66
d. 1.33
Can someone assist me on these few? I got for the first one, c
For the second one d, and fourth one d (1.33)
Thanks
(1) Linear regression assumes:
a. The relationship between X and Y is a straight line.
b. The residuals are normally distributed.
c. The residuals are homoscedastic.
d. Both homoscedastic and normally distributed residuals.
(2) Often times, residual plots as well as other plots of the data will suggest some difficulties or abnormalities in the data. Which of the following statements are not considered difficulties?
a. A nonlinear relationship between X and Y is appropriate.
b. The variance of the error term (and of Y) is constant.
c. The error term does not have a normal distribution.
d. The selected model fits the data well except for very few discrepant or outlying data values, which may have greatly influenced the choice of the regression line.
(3) The Analysis of Variance (ANOVA) table in linear regression can be used to compute:
a. R-Squared
b. Adjusted R-Squared
c. The Overall F statistic
d. R-Squared, Adjusted R-Squared, and the Overall F statistic
(4) Consider a linear regression model with the predictor variables X1, X2, and X3. If we regress X1 on the other two predictor variables X2 and X3 and get an R-Squared value of 0.25, then the corresponding Variance Inflation Factor (VIF) for X1 is:
a. 0.25
b. 0.50
c. 0.66
d. 1.33
Can someone assist me on these few? I got for the first one, c
For the second one d, and fourth one d (1.33)
Thanks