Chapter 3 hehe

In the equation, y=β0+β1 x1+β2 x2+u , β2 is a(n) _____.

• a. independent variable

• b. dependent variable

• c. slope parameter

• d. intercept parameter

Consider the following regression equation: y=β0+β1*x1+ β2*x2+u . What does β1 imply?

• a. β1 measures the ceteris paribus effect of x1 on x2 .

• b. β1 measures the ceteris paribus effect of y on x1 .

• c. β1 measures the ceteris paribus effect of x1 on y .

• d. β1 measures the ceteris paribus effect of x1 on u .

If the explained sum of squares is 35 and the total sum of squares is 49, what is the residual sum of squares?

• a. 10

• b. 12

• c. 18

• d. 14

Which of the following is true of R2?

• a. R2 is also called the standard error of regression.

• b. A low R2 indicates that the Ordinary Least Squares line fits the data well.

• c. R2 usually decreases with an increase in the number of independent variables in a regression.

• d. R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

The value of R2 always _____.

• a. lies below 0

• b. lies above 1

• c. lies between 0 and 1

• d. lies between 1 and 1.5

If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of _____.

• a. perfect collinearity

• b. homoskedasticity

• c. heteroskedasticty

• d. omitted variable bias

The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if _____, where n is the sample size and k is the number of parameters.

• a. n>2

• b. n=k+1

• c. n>k

• d. n<k+1

Exclusion of a relevant variable from a multiple linear regression model leads to the problem of _____.

• a. misspecification of the model

• b. multicollinearity

• c. perfect collinearity

• d. homoskedasticity

Suppose the variable x2 has been omitted from the following regression equation, y=β0+β1 x1+β2 x2+u . ~β1 is the estimator obtained when x2 is omitted from the equation. The bias in ~β1 is positive if _____.

• a. β2 >0 and x1 and x2 are positively correlated

• b. β2 <0 and x1 and x2 are positively correlated

• c. β2 >0 and x1 and x2 are negatively correlated

• d. β2 = 0 and x1 and x2 are negatively correlated

Suppose the variable x2 has been omitted from the following regression equation, y=β0+β1 x1+β2 x2+u . ~β1 is the estimator obtained when x2 is omitted from the equation. The bias in ~β1 is negative if _____.

• a. β2 >0 and x1 and x2 are positively correlated

• b. β2 <0 and x1 and x2 are positively correlated

• c. β2 =0 and x1 and x2 are negatively correlated

• d. β2 =0 and x1 and x2 are negatively correlated