Question 1

We can include a categorical variable (such as region) in a multivariate model in the same way we include a continuous variable.

Accepted Answer

A) True 
 B)False  False

Question 2

Suppose we are interested in the effect of gender on income and estimate a model that includes a dummy variable for men. If we instead estimate a model that includes a dummy variable for women,

Accepted Answer

A)  The coefficient on the dummy variables will be exactly the same. 
B)  The coefficient on the dummy variables will differ across models, but the estimated difference of means between men and women will not change. 
C)  The fit of the model will change. 
D)  We will get different answers when testing a null hypothesis that there is no gender effect. 
A)  The coefficient on the dummy variables will be exactly the same. 
B)  The coefficient on the dummy variables will differ across models, but the estimated difference of means between men and women will not change. 
C)  The fit of the model will change. 
D)  We will get different answers when testing a null hypothesis that there is no gender effect. B

Question 3

When dealing with categorical variables in the context of a multivariate regression, we:

Accepted Answer

A)  Include the categorical variable directly into the model and interpret the results 
B)  Change the categorical variable into a dummy variable (per category) and include all of the dummy variables in the regression. 
C)  Change the categorical variable into a dummy variable (per category) and include all but one of the dummy variables in the regression. 
D)  Interact the categorical variable with a continuous variable. 
A)  Include the categorical variable directly into the model and interpret the results 
B)  Change the categorical variable into a dummy variable (per category) and include all of the dummy variables in the regression. 
C)  Change the categorical variable into a dummy variable (per category) and include all but one of the dummy variables in the regression. 
D)  Interact the categorical variable with a continuous variable. C

Question 4

Explain the reason why we cannot include all of the dummy variables that are part of a given category, and instead need to leave one out (which serves as the reference point).

Accepted Answer

Including all the du

Question 5

It is sometimes useful to "jitter" data when viewing a scatterplot involving dummy variables. To jitter data we

Accepted Answer

A)  Add a small random number to both the independent and dependent variable before running the regression. 
B)  Add a small random number to both the independent and dependent variable to the plotted data. 
C)  Add a small random number to both the independent and dependent variable before running the regression and to the plotted data. 
D)  None of the above. 
A)  Add a small random number to both the independent and dependent variable before running the regression. 
B)  Add a small random number to both the independent and dependent variable to the plotted data. 
C)  Add a small random number to both the independent and dependent variable before running the regression and to the plotted data. 
D)  None of the above.

Question 6

Given the model College GPA = 2.0 + 0.01Parental income + 0.05 Hours studied per week +0.2 Private high school
 where Parental income is measured in thousands of U.S. dollars and Private high school is a dummy variable, answer the following:
a. The expected college GPA of someone who went to private high school, studies for 20 hours a week, and whose parents income is 60,000. 
b. The expected college GPA of someone who did not go to private high school, studies for 5 hours a week, and whose parents income is 100,000. 
c. What is the expected increase in college GPA due to 2 additional hours of studying (holding everything else constant). 
d. What is the expected increase in college GPA associated with having attended a private school rather than a public school?

Accepted Answer

a. (3.8)
b

Question 7

The coefficient on the interaction of a dummy variable and a continuous variable indicates whether slope associated with the continuous variable is different for the group indicated by the dummy variable.

Accepted Answer

A) True 
 B)False

Question 8

Explain how to deal with categorical variables when it comes to running regression analysis, and how to interpret the results. Frame your answer in the context of explaining income (the dependent variable) as a function of region (a categorical independent variable).

Accepted Answer

We have to create dummy variables for ea

Question 9

Consider the following regression results: Income = 30 + 0.2Parental income + 5 Male + 0.001 Male x Parental income and the t-statistics are 3, 3, 3 and 0.2 for the four estimated coefficients (starting with $\beta$0-hat). Which of the following conclusions is correct?

Accepted Answer

A)  Men are not estimated to have greater income than women. 
B)  The fitted value for a man whose parents had 0 income is 30. 
C)  We have no evidence that the effect of parental income differs for men and women. 
D)  The effect of parental income on women is larger than for men. 
A)  Men are not estimated to have greater income than women. 
B)  The fitted value for a man whose parents had 0 income is 30. 
C)  We have no evidence that the effect of parental income differs for men and women. 
D)  The effect of parental income on women is larger than for men.

Question 10

In a bivariate OLS model where the dependent variable is income and the dummy variable is male, the coefficient β1 signifies the difference in the average income between males and females.

Accepted Answer

A) True 
 B)False

Question 11

Given the model Income = β0 + β1Parental income + β2Male
 where Male is a dummy variable, β0=30,000, β1=0.1, and β2=10,000, the expected income of a woman whose parents income is $60,000 is equal to $52,000.

Accepted Answer

A) True 
 B)False

Question 12

We are analyzing the effects of regime type on corruption rates with the following model: Corruption = 10 - 0.1 GDP (per capita) - 2.0 Democracy
Where Corruption is an index of corruption, GDP (per capita) is measured in thousands of dollars, and Democracy is a dummy variable that is equal to one if a country is a democracy and 0 otherwise. What is the expected rate of corruption of a democratic country with a per capita GDP of $50,000?

Accepted Answer

A)  Not enough information to come up with an answer 
B)  8.5 
C)  13.0 
D)  - 4992 
A)  Not enough information to come up with an answer 
B)  8.5 
C)  13.0 
D)  - 4992

Question 13

Given the following regression results: Income = 20,000 + 2,000Years of Experience + 10,000 Male + 1,000Male x Years of experience calculate the following:
a. The expected income of a man with 3 years of work experience 
b. The expected income of a woman with 3 years of work experience
c. The expected income of a man with no work experience

Accepted Answer

a. (39,000

Question 14

Given the model Income = 40 + 1.5Experience - 5Female + 1Experience x Female, at what level of experience will the expected income of men and women be the same (in other words, at what level of experience will the fitted lines for men and women intersect)?

Accepted Answer

A)  At 10 years of experience 
B)  At 2.5 years of experience 
C)  At 5 years of experience 
D)  Not enough information. 
A)  At 10 years of experience 
B)  At 2.5 years of experience 
C)  At 5 years of experience 
D)  Not enough information.

Question 15

The fitted values from a multivariate regression Y0 = β0 + β1Xi + β2Dummy will be

Accepted Answer

A)  Two parallel lines with a slope of β2 and separated by β1. 
B)  Two parallel lines with a slope of β1 and separated by B2. 
C)  Two lines with differing slopes separated by β1 
D)  Two lines with differing slopes separated by β2 
A)  Two parallel lines with a slope of β2 and separated by β1. 
B)  Two parallel lines with a slope of β1 and separated by B2. 
C)  Two lines with differing slopes separated by β1 
D)  Two lines with differing slopes separated by β2

Question 16

Explain the difference between a bivariate and multivariate regression in the contexts of dummy variables.

Accepted Answer

Dummy variables in a bivariate regressio

Question 17

The fitted values from a multivariate regression Y0 = β0 + β1Xi + β2Dummy + β3Dummy*Xi will be

Accepted Answer

A)  Two parallel lines with a slope of B2 and separated by β1. 
B)  Two parallel lines with a slope of β1 and separated by β2. 
C)  Two lines with slopes of β1 and β1+ β3 separated by β2. 
D)  Two lines with slopes of β1and β1+ β3 
A)  Two parallel lines with a slope of B2 and separated by β1. 
B)  Two parallel lines with a slope of β1 and separated by β2. 
C)  Two lines with slopes of β1 and β1+ β3 separated by β2. 
D)  Two lines with slopes of β1and β1+ β3

Question 18

We are analyzing the effects of regime type on corruption rates with the following model: Corruption = 10 - 0.1 GDP (per capita) - 2.0 Democracy
Where Corruption is an index of corruption, GDP (per capita) is measured in thousands of dollars, and Democracy is a dummy variable that is equal to one if a country is a democracy and 0 otherwise. Suppose we want to know the estimated effect on corruption of an extra thousand dollars per capita for a democratic country. Our estimate implies the change in predicted corruption will be

Accepted Answer

A)  0.1 higher 
B)  0.1 lower 
C)  2.1 higher 
D)  2.1 lower 
A)  0.1 higher 
B)  0.1 lower 
C)  2.1 higher 
D)  2.1 lower

Question 19

When conducting analysis using categorical variables, we include a dummy variable for every category covered by the categorical variable.

Accepted Answer

A) True 
 B)False

Question 20

Which of the following is NOT a categorical variable:

Accepted Answer

A)  Race 
B)  Region 
C)  Level of Education 
D)  Car manufacturer 
A)  Race 
B)  Region 
C)  Level of Education 
D)  Car manufacturer

We can include a categorical variable (such as region) in a multivariate model in the same way we include a continuous variable.

Suppose we are interested in the effect of gender on income and estimate a model that includes a dummy variable for men. If we instead estimate a model that includes a dummy variable for women,

When dealing with categorical variables in the context of a multivariate regression, we:

Explain the reason why we cannot include all of the dummy variables that are part of a given category, and instead need to leave one out (which serves as the reference point).

It is sometimes useful to "jitter" data when viewing a scatterplot involving dummy variables. To jitter data we

The coefficient on the interaction of a dummy variable and a continuous variable indicates whether slope associated with the continuous variable is different for the group indicated by the dummy variable.

Explain how to deal with categorical variables when it comes to running regression analysis, and how to interpret the results. Frame your answer in the context of explaining income (the dependent variable) as a function of region (a categorical independent variable).

Consider the following regression results: Income = 30+ 0.2Parental income + 5 Male + 0.001 Male x Parental income and the t-statistics are 3, 3, 3 and 0.2 for the four estimated coefficients (starting with $\beta$ ₀-hat). Which of the following conclusions is correct?

In a bivariate OLS model where the dependent variable is income and the dummy variable is male, the coefficient β₁ signifies the difference in the average income between males and females.

Given the model Income = β₀+ β₁Parental income + β₂Male where Male is a dummy variable, β₀=30,000, β₁=0.1, and β₂=10,000, the expected income of a woman whose parents income is $60,000 is equal to $52,000.

Given the model Income = 40 + 1.5Experience - 5Female + 1Experience x Female, at what level of experience will the expected income of men and women be the same (in other words, at what level of experience will the fitted lines for men and women intersect)?

The fitted values from a multivariate regression Y₀= β₀+ β₁X_i+ β₂Dummy will be

Explain the difference between a bivariate and multivariate regression in the contexts of dummy variables.

The fitted values from a multivariate regression Y₀= β₀+ β₁X_i+ β₂Dummy + β₃Dummy*X_i will be

When conducting analysis using categorical variables, we include a dummy variable for every category covered by the categorical variable.

Which of the following is NOT a categorical variable:

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research

Multivariate Ols: Where the Action Is

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

Instrumental Variables: Using Exogenous Variation to Fight Endogen

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

Exam 6: Dummy Variables: Smarter Than You Think

We can include a categorical variable (such as region) in a multivariate model in the same way we include a continuous variable.

Suppose we are interested in the effect of gender on income and estimate a model that includes a dummy variable for men. If we instead estimate a model that includes a dummy variable for women,

When dealing with categorical variables in the context of a multivariate regression, we:

Explain the reason why we cannot include all of the dummy variables that are part of a given category, and instead need to leave one out (which serves as the reference point).

It is sometimes useful to "jitter" data when viewing a scatterplot involving dummy variables. To jitter data we

The coefficient on the interaction of a dummy variable and a continuous variable indicates whether slope associated with the continuous variable is different for the group indicated by the dummy variable.

Explain how to deal with categorical variables when it comes to running regression analysis, and how to interpret the results. Frame your answer in the context of explaining income (the dependent variable) as a function of region (a categorical independent variable).

Consider the following regression results: Income = 30 + 0.2Parental income + 5 Male + 0.001 Male x Parental income and the t-statistics are 3, 3, 3 and 0.2 for the four estimated coefficients (starting with β\betaβ 0-hat). Which of the following conclusions is correct?

In a bivariate OLS model where the dependent variable is income and the dummy variable is male, the coefficient β1 signifies the difference in the average income between males and females.

Given the model Income = β0 + β1Parental income + β2Male where Male is a dummy variable, β0=30,000, β1=0.1, and β2=10,000, the expected income of a woman whose parents income is $60,000 is equal to $52,000.

Given the model Income = 40 + 1.5Experience - 5Female + 1Experience x Female, at what level of experience will the expected income of men and women be the same (in other words, at what level of experience will the fitted lines for men and women intersect)?

The fitted values from a multivariate regression Y0 = β0 + β1Xi + β2Dummy will be

Explain the difference between a bivariate and multivariate regression in the contexts of dummy variables.

The fitted values from a multivariate regression Y0 = β0 + β1Xi + β2Dummy + β3Dummy*Xi will be

When conducting analysis using categorical variables, we include a dummy variable for every category covered by the categorical variable.

Which of the following is NOT a categorical variable:

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research

Multivariate Ols: Where the Action Is

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

Instrumental Variables: Using Exogenous Variation to Fight Endogen

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

Consider the following regression results: Income = 30+ 0.2Parental income + 5 Male + 0.001 Male x Parental income and the t-statistics are 3, 3, 3 and 0.2 for the four estimated coefficients (starting with $\beta$ ₀-hat). Which of the following conclusions is correct?

In a bivariate OLS model where the dependent variable is income and the dummy variable is male, the coefficient β₁ signifies the difference in the average income between males and females.

Given the model Income = β₀+ β₁Parental income + β₂Male where Male is a dummy variable, β₀=30,000, β₁=0.1, and β₂=10,000, the expected income of a woman whose parents income is $60,000 is equal to $52,000.

The fitted values from a multivariate regression Y₀= β₀+ β₁X_i+ β₂Dummy will be

The fitted values from a multivariate regression Y₀= β₀+ β₁X_i+ β₂Dummy + β₃Dummy*X_i will be