1. In your regression model, if you detect autocorrelation, it means that the errors:

a. Are independent of each other

b.Are not independent of each other

c.Are not normally distributed

d.Are normally distributed

QUESTION 2

In a regression, if you find that errors get larger as the independent variable gets larger, this is called:

1. Heteroscedasticity
2. Autocorrelation
3. Multicollinearity
4. Homoscedasticity

QUESTION 3

1. In your regression model, if two or more independent variables are highly correlated, you have a issue of:
2. Heteroscedasticity
3. Multicollinearity
4. Homoscedasticity
5. Autocorrelation

2 points

QUESTION 4

1. (From last week's lecture but related to this week's reading), if you have a categorical variable that contains four categories, how many dummy variables will you use in the regression?
2. Three
3. Five
4. One
5. Four

QUESTION 5

1. In order to estimate a regression equation, we assume that the errors are:
2. Uniformly distributed with a constant mean
3. Uniformly distributed with a mean of zero
4. Normally distributed with a constant mean
5. Normally distributed with a mean of zero