Question 1

It is often times necessary to make reference to a base group in regression analysis, which denotes what?

Accepted Answer

A)  The preferred selection of control variables to have in the regression equation. 
B)  The set of homoscedastic errors. 
C)  The excluded dummy variable among a set of dummy variables representing a categorical, ordinal or interval variable. 
D)  The control group in a randomized control trial. 
A)  The preferred selection of control variables to have in the regression equation. 
B)  The set of homoscedastic errors. 
C)  The excluded dummy variable among a set of dummy variables representing a categorical, ordinal or interval variable. 
D)  The control group in a randomized control trial.

Question 2

The successful use of a proxy variable to control for a confounding factor will allow you to accomplish all of the following except:

Accepted Answer

A)  consistently estimate the treatment effect of interest. 
B)  limit the endogeneity problem associated with the confounding factor. 
C)  uncover the size of the semi-partial correlation of the confounding factor and the outcome. 
D)  conduct appropriate hypothesis testing for the treatment effect of interest. 
A)  consistently estimate the treatment effect of interest. 
B)  limit the endogeneity problem associated with the confounding factor. 
C)  uncover the size of the semi-partial correlation of the confounding factor and the outcome. 
D)  conduct appropriate hypothesis testing for the treatment effect of interest.

Question 3

In estimating the effect of price on sales, what is likely to be a confounding factor that one would at best have only a proxy variable for?

Accepted Answer

A)  Price 
B)  Sales 
C)  Month 
D)  Brand awareness 
A)  Price 
B)  Sales 
C)  Month 
D)  Brand awareness

Question 4

Suppose the fitted (assumed causal) regression line for your data is as follows: Log(Output) = 6.2 + 0.4 × Log(Labor) + 0.5 × Log(Capital)
Interpret the coefficient on Log(Labor).

Accepted Answer

A)  Holding capital fixed, when labor goes up by 1, output goes up by 0.4 
B)  Holding capital fixed, when labor goes up by 1, output goes up by 6.6 
C)  Holding capital fixed, when labor goes up by 1%, output goes up by 0.4% 
D)  Holding capital fixed, when labor goes up by 1%, output goes up by 40 units 
A)  Holding capital fixed, when labor goes up by 1, output goes up by 0.4 
B)  Holding capital fixed, when labor goes up by 1, output goes up by 6.6 
C)  Holding capital fixed, when labor goes up by 1%, output goes up by 0.4% 
D)  Holding capital fixed, when labor goes up by 1%, output goes up by 40 units

Question 5

Which of the following correlations would be an acceptable way to show that the variable (Pi) is a suitable proxy for Ai in the regression Yi = β0 + β1Ti + β2Ai + Ui?

Accepted Answer

A)  Corr(Yi, Ti) ≠ 0 
B)  spCorr(Yi, Ti (Pi)) ≠ 0 
C)  pCorr(Yi, Pi; Ti) ≠ 0 
D)  None of the answers is correct. 
A)  Corr(Yi, Ti) ≠ 0 
B)  spCorr(Yi, Ti (Pi)) ≠ 0 
C)  pCorr(Yi, Pi; Ti) ≠ 0 
D)  None of the answers is correct.

Question 6

Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $$\begin{array} { | l | r | r | } 
\hline & \text { Coefficierts } & \text { Stardard Error } \
\hline \text { Iratercept } & 16310.0 & 4114.5 \
\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \
\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \
\hline
\end{array}$$ r-squared = 0.302 Adjusted r-squared = 0.299 If we assume that the proper model to predict the market value of houses is given by this regression, and we also happen to know that number of bathrooms and number of bedrooms is uncorrelated both in the sample and in the target population of house sales, why might we still want to include number of bathrooms in a regression to identify the causal effect of number of bedrooms on home prices?

Accepted Answer

A)  It lowers the adjusted r-squared. 
B)  It increases the r-squared value. 
C)  It provides a sanity check on our regression. 
D)  It also leads to more consistent estimates of the treatment effect. 
A)  It lowers the adjusted r-squared. 
B)  It increases the r-squared value. 
C)  It provides a sanity check on our regression. 
D)  It also leads to more consistent estimates of the treatment effect.

Question 7

A dummy variable is:

Accepted Answer

A)  a variable with a low t-stat and high p-value. 
B)  a placeholder for a variable to be gathered later. 
C)  a dichotomous variable that is used to indicate the presence or absence of a characteristic. 
D)  an unbiased estimate of the true variable. 
A)  a variable with a low t-stat and high p-value. 
B)  a placeholder for a variable to be gathered later. 
C)  a dichotomous variable that is used to indicate the presence or absence of a characteristic. 
D)  an unbiased estimate of the true variable.

Question 8

Suppose you've regressed profits on price, assuming a quadratic functional form. Your regression equation is: Profitsi = β0 + β1Pricei + β2Pricei2 + Ui. What is the marginal effect of price in this equation?

Accepted Answer

A)  β0 + β1 + β2 
B)  β1 + β2 × Pricei 
C)  β0 + β1 + 2β2 
D)  β1 + 2β2 × Pricei 
A)  β0 + β1 + β2 
B)  β1 + β2 × Pricei 
C)  β0 + β1 + 2β2 
D)  β1 + 2β2 × Pricei

Question 9

The percentage change in one variable with a percentage change in another is known as a(n):

Accepted Answer

A)  percentage point change. 
B)  log-level change. 
C)  elasticity. 
D)  linear effect. 
A)  percentage point change. 
B)  log-level change. 
C)  elasticity. 
D)  linear effect.

It is often times necessary to make reference to a base group in regression analysis, which denotes what?

The successful use of a proxy variable to control for a confounding factor will allow you to accomplish all of the following except:

In estimating the effect of price on sales, what is likely to be a confounding factor that one would at best have only a proxy variable for?

Suppose the fitted (assumed causal) regression line for your data is as follows: Log(Output) = 6.2 + 0.4 × Log(Labor) + 0.5 × Log(Capital) Interpret the coefficient on Log(Labor).

Which of the following correlations would be an acceptable way to show that the variable (P_i) is a suitable proxy for A_iin the regression Y_i = β₀ + β₁T_i + β₂A_i + U_i?

A dummy variable is:

Suppose you've regressed profits on price, assuming a quadratic functional form. Your regression equation is: Profits_i = β₀ + β₁Price_i + β₂Price_i² + U_i. What is the marginal effect of price in this equation?

The percentage change in one variable with a percentage change in another is known as a(n):

The Roles of Data and Predictive Analytics in Business

Reasoning With Data

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters

Exam 7: Basic Methods for Establishing Causal Inference

It is often times necessary to make reference to a base group in regression analysis, which denotes what?

The successful use of a proxy variable to control for a confounding factor will allow you to accomplish all of the following except:

In estimating the effect of price on sales, what is likely to be a confounding factor that one would at best have only a proxy variable for?

Suppose the fitted (assumed causal) regression line for your data is as follows: Log(Output) = 6.2 + 0.4 × Log(Labor) + 0.5 × Log(Capital) Interpret the coefficient on Log(Labor).

Which of the following correlations would be an acceptable way to show that the variable (Pi) is a suitable proxy for Ai in the regression Yi = β0 + β1Ti + β2Ai + Ui?

A dummy variable is:

Suppose you've regressed profits on price, assuming a quadratic functional form. Your regression equation is: Profitsi = β0 + β1Pricei + β2Pricei2 + Ui. What is the marginal effect of price in this equation?

The percentage change in one variable with a percentage change in another is known as a(n):

The Roles of Data and Predictive Analytics in Business

Reasoning With Data

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters

Which of the following correlations would be an acceptable way to show that the variable (P_i) is a suitable proxy for A_iin the regression Y_i = β₀ + β₁T_i + β₂A_i + U_i?

Suppose you've regressed profits on price, assuming a quadratic functional form. Your regression equation is: Profits_i = β₀ + β₁Price_i + β₂Price_i² + U_i. What is the marginal effect of price in this equation?