Question 1

Irrelevant variables are ones that:

Accepted Answer

A)  do not affect the outcome. 
B)  affect the outcome but only in the population. 
C)  affect the outcome in the sample. 
D)  have variables with low p-values. 
A)  do not affect the outcome. 
B)  affect the outcome but only in the population. 
C)  affect the outcome in the sample. 
D)  have variables with low p-values.

Question 2

Which of the following cannot be estimated using traditional linear regression techniques?

Accepted Answer

A)  log(Y) = β0 + β1log(X) + Ui 
B)  log(Y) = β0 + β1X + Ui 
C)  Y = β0 + β1log(X) + Ui 
D)  None of the answers is correct. 
A)  log(Y) = β0 + β1log(X) + Ui 
B)  log(Y) = β0 + β1X + Ui 
C)  Y = β0 + β1log(X) + Ui 
D)  None of the answers is correct.

Question 3

Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $$\begin{array} { | l | r | r | } 
\hline & \text { Coefficierts } & \text { Stardard Error } \
\hline \text { Iratercept } & 16310.0 & 4114.5 \
\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \
\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \
\hline
\end{array}$$ Given these results which additional condition would be sufficient to ensure number of bathrooms satisfies the "primary criteria" for a good control variable in attempting to identify the causal effect of number of bedrooms on house prices?

Accepted Answer

A)  The number of bathrooms is correlated with house prices. 
B)  The number of bedrooms is correlated with house prices. 
C)  The number of bathrooms is correlated with the number of bedrooms. 
D)  The p-value for the number of bedrooms. 
A)  The number of bathrooms is correlated with house prices. 
B)  The number of bedrooms is correlated with house prices. 
C)  The number of bathrooms is correlated with the number of bedrooms. 
D)  The p-value for the number of bedrooms.

Question 4

What condition best describes the endogeneity problem?

Accepted Answer

A)  The variance of the errors (Ui) depends on Xi. 
B)  Some variables within Xi are perfectly correlated with other variables in Xi. 
C)  The distribution of the errors (Ui) is non-normal. 
D)  One of the Xi variables is correlated with the error term (Ui). 
A)  The variance of the errors (Ui) depends on Xi. 
B)  Some variables within Xi are perfectly correlated with other variables in Xi. 
C)  The distribution of the errors (Ui) is non-normal. 
D)  One of the Xi variables is correlated with the error term (Ui).

Question 5

Which step is not involved with constructing a representative sample?

Accepted Answer

A)  Choose the independent variables you want to match. 
B)  Collect the sample by randomly sampling from each stratum (defined by your pre-chosen independent variables). 
C)  Use information about the population to stratify (categorize) each of the chosen variables. 
D)  Run a t-test to check if the outcomes are different across stratums. 
A)  Choose the independent variables you want to match. 
B)  Collect the sample by randomly sampling from each stratum (defined by your pre-chosen independent variables). 
C)  Use information about the population to stratify (categorize) each of the chosen variables. 
D)  Run a t-test to check if the outcomes are different across stratums.

Question 6

The determining function that drives share of accepted job offers for a company is given by the following equation: AcceptedOfferst = α0 + α1StartingSalaryt + α2EconomicClimatet + Ut, where the unit of observation is particular month (t). Suppose one wanted to use the national unemployment rate (unemploymentt) as a proxy for EconomicClimate. Which of the following describes a condition required to hold for this to be an adequate proxy variable?

Accepted Answer

A)  α2 t) 
C)  α2 > 0 
D)  EconomicClimate is correlated with StartingSalary 
A)  α2 t) 
C)  α2 > 0 
D)  EconomicClimate is correlated with StartingSalary

Question 7

In attempting to estimate the causal effect of employee hours worked on total units produced, you include the age of the production equipment as a control variable. Which of the following conditions would not be a good reason to include this control variable?

Accepted Answer

A)  Older production equipment often requires more employees to work on them. 
B)  Older production equipment often is less productive (i.e., produce fewer units on average). 
C)  The age distribution of the plants is highly non-normal. 
D)  The semi-partial correlation of total units produced on age of the machine holding number of employees fixed is positive and statistically significant. 
A)  Older production equipment often requires more employees to work on them. 
B)  Older production equipment often is less productive (i.e., produce fewer units on average). 
C)  The age distribution of the plants is highly non-normal. 
D)  The semi-partial correlation of total units produced on age of the machine holding number of employees fixed is positive and statistically significant.

Question 8

Which of the following regressions would yield a coefficient estimate that would be directly interpreted as the price elasticity of demand?

Accepted Answer

A)  Quantityi = α0 + α1log(Pricei) 
B)  log(Quantityi) = α0 + α1log(Pricei) 
C)  Quantityi = α0 + α1Pricei 
D)  Quantityi = α0 - α1log(Pricei) 
A)  Quantityi = α0 + α1log(Pricei) 
B)  log(Quantityi) = α0 + α1log(Pricei) 
C)  Quantityi = α0 + α1Pricei 
D)  Quantityi = α0 - α1log(Pricei)

Question 9

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 thus we are getting unbiased estimates of the true relationships between number of bedrooms/bathrooms and sales price, what is the effect on sales price of increasing the number of bathrooms in a house by one, holding number of bedrooms fixed?

Accepted Answer

A)  Approximately, $16,310 dollars. 
B)  Approximately, $ 7,295 + 23,473 = $30,768. 
C)  Approximately, $23,473. 
D)  Approximately, an increase of 30%. 
A)  Approximately, $16,310 dollars. 
B)  Approximately, $ 7,295 + 23,473 = $30,768. 
C)  Approximately, $23,473. 
D)  Approximately, an increase of 30%.

Question 10

The determining function that drives movie ticket sales is given by the following equation: Salesit = α0 + α1 HolidaySeasonit + α2CastAwarenessit + Uit, where the unit of observation is a particular movie (i) in month (t). Suppose one wanted to use cumulative past movie appearances of the entire cast of a movie as a proxy for CastAwareness. Which of the following conditions would not be one required for this to be an adequate proxy variable?

Accepted Answer

A)  CumulativePastMovieAppearances to be correlated with CastAwareness. 
B)  HolidaySeason, CastAwareness, and CumulativePastMovieAppearances to be uncorrelated with "other factors" (Uit). 
C)  CastAwareness to be uncorrelated with HolidaySeason. 
D)  HolidaySeason and CumulativePastMovieAppearances to be uncorrelated with "other factors" affecting CastAwareness. 
A)  CumulativePastMovieAppearances to be correlated with CastAwareness. 
B)  HolidaySeason, CastAwareness, and CumulativePastMovieAppearances to be uncorrelated with "other factors" (Uit). 
C)  CastAwareness to be uncorrelated with HolidaySeason. 
D)  HolidaySeason and CumulativePastMovieAppearances to be uncorrelated with "other factors" affecting CastAwareness.

Question 11

A proxy variable is a variable used in a regression equation in order to:

Accepted Answer

A)  report the r-squared value appropriately. 
B)  make the adjustment for the adjusted r-squared. 
C)  proxy for a confounding factor in an attempt to alleviate the endogeneity problem. 
D)  improve the standard errors on the treatment effect. 
A)  report the r-squared value appropriately. 
B)  make the adjustment for the adjusted r-squared. 
C)  proxy for a confounding factor in an attempt to alleviate the endogeneity problem. 
D)  improve the standard errors on the treatment effect.

Question 12

Which of the following variables might be a proxy variable for the confounding factor of cognitive ability in a sample of workers and their ability to generate sales?

Accepted Answer

A)  Test scores 
B)  Past sales records 
C)  Tenure at the company 
D)  Age 
A)  Test scores 
B)  Past sales records 
C)  Tenure at the company 
D)  Age

Question 13

The determining function that drives share of accepted job offers for a company is given by the following equation: AcceptedOfferst = α0 + α1StartingSalaryt + α2EconomicClimatet + Ut, where the unit of observation is particular month (t). Suppose one wanted to use the national unemployment rate (unemploymentt) as a proxy for EconomicClimate. Which of the following would be a condition that would rule out the unemployment rate being a good proxy for economic climate?

Accepted Answer

A)  α2 t) 
C)  α2 > 0 
D)  EconomicClimate is correlated with StartingSalary 
A)  α2 t) 
C)  α2 > 0 
D)  EconomicClimate is correlated with StartingSalary

Question 14

In determining the causal effect of Price on Sales, if advertising spend is a good control variable, how will the correlation of the error terms from the regression, Salesi = β0 + β1Pricei + Ui, and Salesi = α0 + α1Pricei + α2Advertisingi + Vi and price be related?

Accepted Answer

A)  Cor(Ui, Vi) = 0 
B)  Cor(Ui, Pricei) = 0, Cor(Pricei, Vi) = 0 
C)  Cor(Ui, Pricei) ≠ 0, Cor(Pricei, Vi) = 0 
D)  Cor(Ui, Pricei) = 0, Cor(Pricei, Vi) ≠ 0 
A)  Cor(Ui, Vi) = 0 
B)  Cor(Ui, Pricei) = 0, Cor(Pricei, Vi) = 0 
C)  Cor(Ui, Pricei) ≠ 0, Cor(Pricei, Vi) = 0 
D)  Cor(Ui, Pricei) = 0, Cor(Pricei, Vi) ≠ 0

Question 15

Does the interpretation on the estimated coefficient on the treatment change if you use a proxy variable to control for a confounding factor?

Accepted Answer

A)  No, the estimated coefficient is a consistent estimate of the causal effect. 
B)  No, but instead of getting causal estimate you're only estimating partial correlations. 
C)  Yes, the coefficient on the treatment is biased. 
D)  Yes, the coefficient on the treatment suffers from the simultaneity bias. 
A)  No, the estimated coefficient is a consistent estimate of the causal effect. 
B)  No, but instead of getting causal estimate you're only estimating partial correlations. 
C)  Yes, the coefficient on the treatment is biased. 
D)  Yes, the coefficient on the treatment suffers from the simultaneity bias.

Question 16

Suppose you have estimated the following regression: Refrigerator Salesi = β0 + β1Pricei + Ui. However, when presenting your results, someone in the audience claims your estimate is biased because your sample only contains sales figures for refrigerators that are priced over $300. Their point is:

Accepted Answer

A)  correct. You have a selected sample on the Y variable, so your estimate is biased. 
B)  correct. You have a selected sample on the X variable, so your estimate is biased. 
C)  incorrect. You have a selected sample on Y variable, but this does not bias your estimate. 
D)  incorrect. You have a selected sample on X variable, but this does not bias your estimate. 
A)  correct. You have a selected sample on the Y variable, so your estimate is biased. 
B)  correct. You have a selected sample on the X variable, so your estimate is biased. 
C)  incorrect. You have a selected sample on Y variable, but this does not bias your estimate. 
D)  incorrect. You have a selected sample on X variable, but this does not bias your estimate.

Question 17

In estimating the effect of a difference in earnings for ivy league graduates, non-ivy league college graduates, and non-college graduates the following regression is run: Earningsi = β0 + β1IvyLeaguei + β2NonIvyCollegeGraduatei + Ui. What does the coefficient β1 represent?

Accepted Answer

A)  The additional increase (decrease) in earnings from going from graduating college to graduating from an Ivy league school. 
B)  The average earnings of an Ivy league graduate. 
C)  The additional increase (decrease) in earnings from going from no college degree to an Ivy league degree. 
D)  The average earnings of an ivy league graduate conditional on graduating from any college. 
A)  The additional increase (decrease) in earnings from going from graduating college to graduating from an Ivy league school. 
B)  The average earnings of an Ivy league graduate. 
C)  The additional increase (decrease) in earnings from going from no college degree to an Ivy league degree. 
D)  The average earnings of an ivy league graduate conditional on graduating from any college.

Question 18

A representative sample is one whose:

Accepted Answer

A)  distribution is approximately normal as it gets larger. 
B)  sample is drawn randomly. 
C)  distribution approximately matches that of the population, for a subset of observed, independent variables. 
D)  distribution exactly matches the population distribution for the outcome variable. 
A)  distribution is approximately normal as it gets larger. 
B)  sample is drawn randomly. 
C)  distribution approximately matches that of the population, for a subset of observed, independent variables. 
D)  distribution exactly matches the population distribution for the outcome variable.

Question 19

An ordinal variable is one that contains categories that:

Accepted Answer

A)  have an obvious order, but the difference in values is not meaningful. 
B)  do not have an obvious ordering. 
C)  have an obvious ordering, and the difference in values is meaningful. 
D)  None of the answers is correct. 
A)  have an obvious order, but the difference in values is not meaningful. 
B)  do not have an obvious ordering. 
C)  have an obvious ordering, and the difference in values is meaningful. 
D)  None of the answers is correct.

Question 20

The use of a proxy variable changes how you must interpret which of the following statistics?

Accepted Answer

A)  R-squared 
B)  P-value of coefficient on treatment 
C)  Standard errors on treatment 
D)  None of the answers is correct. 
A)  R-squared 
B)  P-value of coefficient on treatment 
C)  Standard errors on treatment 
D)  None of the answers is correct.

Irrelevant variables are ones that:

Which of the following cannot be estimated using traditional linear regression techniques?

What condition best describes the endogeneity problem?

Which step is not involved with constructing a representative sample?

In attempting to estimate the causal effect of employee hours worked on total units produced, you include the age of the production equipment as a control variable. Which of the following conditions would not be a good reason to include this control variable?

Which of the following regressions would yield a coefficient estimate that would be directly interpreted as the price elasticity of demand?

A proxy variable is a variable used in a regression equation in order to:

Which of the following variables might be a proxy variable for the confounding factor of cognitive ability in a sample of workers and their ability to generate sales?

In determining the causal effect of Price on Sales, if advertising spend is a good control variable, how will the correlation of the error terms from the regression, Sales_i = β₀ + β₁Price_i + U_i, and Sales_i = α₀ + α₁Price_i + α₂Advertising_i + V_i and price be related?

Does the interpretation on the estimated coefficient on the treatment change if you use a proxy variable to control for a confounding factor?

In estimating the effect of a difference in earnings for ivy league graduates, non-ivy league college graduates, and non-college graduates the following regression is run: Earnings_i = β₀ + β₁IvyLeague_i + β₂NonIvyCollegeGraduate_i + U_i. What does the coefficient β₁represent?

A representative sample is one whose:

An ordinal variable is one that contains categories that:

The use of a proxy variable changes how you must interpret which of the following statistics?

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

Irrelevant variables are ones that:

Which of the following cannot be estimated using traditional linear regression techniques?

What condition best describes the endogeneity problem?

Which step is not involved with constructing a representative sample?

In attempting to estimate the causal effect of employee hours worked on total units produced, you include the age of the production equipment as a control variable. Which of the following conditions would not be a good reason to include this control variable?

Which of the following regressions would yield a coefficient estimate that would be directly interpreted as the price elasticity of demand?

A proxy variable is a variable used in a regression equation in order to:

Which of the following variables might be a proxy variable for the confounding factor of cognitive ability in a sample of workers and their ability to generate sales?

In determining the causal effect of Price on Sales, if advertising spend is a good control variable, how will the correlation of the error terms from the regression, Salesi = β0 + β1Pricei + Ui, and Salesi = α0 + α1Pricei + α2Advertisingi + Vi and price be related?

Does the interpretation on the estimated coefficient on the treatment change if you use a proxy variable to control for a confounding factor?

Suppose you have estimated the following regression: Refrigerator Salesi = β0 + β1Pricei + Ui. However, when presenting your results, someone in the audience claims your estimate is biased because your sample only contains sales figures for refrigerators that are priced over $300. Their point is:

In estimating the effect of a difference in earnings for ivy league graduates, non-ivy league college graduates, and non-college graduates the following regression is run: Earningsi = β0 + β1IvyLeaguei + β2NonIvyCollegeGraduatei + Ui. What does the coefficient β1 represent?

A representative sample is one whose:

An ordinal variable is one that contains categories that:

The use of a proxy variable changes how you must interpret which of the following statistics?

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

In determining the causal effect of Price on Sales, if advertising spend is a good control variable, how will the correlation of the error terms from the regression, Sales_i = β₀ + β₁Price_i + U_i, and Sales_i = α₀ + α₁Price_i + α₂Advertising_i + V_i and price be related?

In estimating the effect of a difference in earnings for ivy league graduates, non-ivy league college graduates, and non-college graduates the following regression is run: Earnings_i = β₀ + β₁IvyLeague_i + β₂NonIvyCollegeGraduate_i + U_i. What does the coefficient β₁represent?