# Multiple Choice Quizzes

Take the quiz test your understanding of the key concepts covered in the chapter. Try testing yourself before you read the chapter to see where your strengths and weaknesses are, then test yourself again once you’ve read the chapter to see how well you’ve understood.

1. Which one of these statements is not a Gauss-Markov assumption?

1. That the error term has a conditional mean of zero
2. Absence of influential observations.
3. That the error term has constant variance
4. That the errors are uncorrelated

b. Absence of influential observations

2. Why should we not include irrelevant variables in our regression analysis?

1. Your R-squared will become too high
2. Because of data limitations
4. We increase the risk of producing false significant results

d. We increase the risk of producing false significant results

3. How can we deal with the breach of the assumption of linearity?

1. Include a squared term
2. Include an interaction term
3. Use robust regression
4. Use the margins command

a. Include a squared term

4. What is the best way to find the exact top or bottom point of a squared effect?

1. Through derivation using values from the two coefficients
2. Excluding the squared term and predicting
3. Including the squared term and predicting
4. Graphing the results and comparing the top/bottom point with the value on the X-axis

a. Through derivation using values from the two coefficients

5. Name another way of modeling non-linearity

2. Using an interaction term
3. Using dummy variables
4. Using a bivariate regression model

c. Using dummy variables

6. Which statistics can help us detect multicollinarity?

1. Variance inflation factor (VIF)
2. F-statistic
3. Durbin-Watson
4. Tolerance values (1-VIF)

a. Variance inflation factor (VIF)

d. Tolerance values (1-VIF)

7. What does heteroskedasticity mean?

1. The variance in the residuals are the same regardless of their predicted values.
2. There is variance in the residuals
3. That we are unable to produce residuals
4. The variance in the residuals differ depending on their predicted values

d. The variance in the residuals differ depending on their predicted values

8. What are the two ways we can check for heteroskedasticity?

1. We can examine a plot of predicted values vs the residuals
2. We can run the Hausman test
3. We can run the hettest command
4. We can compare the F-test of two models

a. We can examine a plot of predicted values vs the residuals

c. We can run the hettest command

9. Which one is not a measure of influential (or potentially influential) units?

1. Leverage
2. Cook-Weisberg
3. DFBETA
4. Cook’s distance