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. What does a multiple linear regression analysis examine?

The relationship between more than one dependent and only one independent variable

The relationship between one or more than one dependent and only one independent variable

The relationship between one dependent and more than one independent variables

The relationship between more than one independent variables

Answer:

c. The relationship between one dependent and more than one independent variables

2. What does the following expression (H_{0}: β_{1} = β_{2} = 0) mean?

One of the independent variables is useful in predicting the dependent variable

Both of the independent variables are useful in predicting the dependent variable

None of the independent variables is useful in predicting the dependent variable

There is a third independent variable predicting the dependent variable

Answer:

c. None of the independent variables is useful in predicting the dependent variable

3. Which of the following criteria is the most optimal for assessing the goodness of the fit of a multiple linear regression model?

Adjusted R2

R2

The intercept

The coefficient

Answer:

a.Adjusted R2

4. In which cases are the standardised coefficients suggested to be used to identify the relative importance of the independent variables in a multiple regression model?

When all the independent variables are measured using the same metric

When not all the independent variables are measured using the same metric

When not all the independent variables are measured using the same metric

When all the independent variables are measured using an ordinal scale ranging from 1 to 6

Answer:

b. When not all the independent variables are measured using the same metric

c. When not all the independent variables are measured using the same metric

5. What is the post estimation command that you can use after the regress command in Stata to compute the predicted mean-Y values of interest?