Question 1

What are the two key assumptions necessary to establish causality from a regression model?

Accepted Answer

A)  Random sample of participants and targeted assignment of the treatment 
B)  Random sample of participants and random assignment of the treatment 
C)  Targeted sample of participants and random assignment of the treatment 
D)  Targeted sample of participants and targeted assignment of the treatment 
A)  Random sample of participants and targeted assignment of the treatment 
B)  Random sample of participants and random assignment of the treatment 
C)  Targeted sample of participants and random assignment of the treatment 
D)  Targeted sample of participants and targeted assignment of the treatment

Question 2

In the following determining function, Earningsi = α0 + α1Educationi + Ui, what might be a factor contained in Ui?

Accepted Answer

A)  Predictions of earnings 
B)  Innate ability, which might be correlated with education and earnings 
C)  Proximity to a 4-year college, which is known to not impact earnings 
D)  Heteroscedasticity 
A)  Predictions of earnings 
B)  Innate ability, which might be correlated with education and earnings 
C)  Proximity to a 4-year college, which is known to not impact earnings 
D)  Heteroscedasticity

Question 3

In estimating the causal relationship between Sales and Price in the following determining function Salesi = β0 + β1Pricei + Ui, what assumption in addition to E [Ui ] = 0, justifies that the estimated coefficient on Price can be interpreted as an estimate of the causal effect of Price on Sales?

Accepted Answer

A)  E [PriceiUi ] = 0 
B)  E [SalesiUi ] = 0 
C)  E [PriceiUi ] = β0 
D)  E [PriceiUi ] = β1 
A)  E [PriceiUi ] = 0 
B)  E [SalesiUi ] = 0 
C)  E [PriceiUi ] = β0 
D)  E [PriceiUi ] = β1

Question 4

In the determining function for Labor productivity given by Labor Prodi = α0 + α1Agei + α2Educationi + Ui, what is the role of Ui?

Accepted Answer

A)  Factors other than age and education that affect labor productivity 
B)  Factors correlated with age but not education, and do not impact labor productivity 
C)  Factors uncorrelated with age and education, and do not impact labor productivity 
D)  The joint impact of age and education on labor productivity 
A)  Factors other than age and education that affect labor productivity 
B)  Factors correlated with age but not education, and do not impact labor productivity 
C)  Factors uncorrelated with age and education, and do not impact labor productivity 
D)  The joint impact of age and education on labor productivity

Question 5

Which of the following formulas is the correct calculation for the unconditional correlation between variables X and Y?

Accepted Answer

A)  $\frac { \operatorname { sCov } ( X , Y ) } { N - 1 }$ 
B)  $$\frac { \operatorname { sCov } ( X , Y ) } { \sqrt { N - 1 } }$$ 
C)  $$\frac { s \operatorname { Cov } ( X , Y ) } { \sqrt { s \operatorname { Var } ( X ) s \operatorname { Var } ( Y ) } }$$ 
D)  $\frac { s \operatorname { Cov } ( X , Y ) } { \sqrt { s \operatorname { Var } ( X ) } }$ 
A)  $\frac { \operatorname { sCov } ( X , Y ) } { N - 1 }$ 
B)  $$\frac { \operatorname { sCov } ( X , Y ) } { \sqrt { N - 1 } }$$ 
C)  $$\frac { s \operatorname { Cov } ( X , Y ) } { \sqrt { s \operatorname { Var } ( X ) s \operatorname { Var } ( Y ) } }$$ 
D)  $\frac { s \operatorname { Cov } ( X , Y ) } { \sqrt { s \operatorname { Var } ( X ) } }$

Question 6

To calculate the semi-partial correlation of Coconut Milk household purchases CMi and Salami Deli Meat household purchases Si controlling for household income Yi, (i.e., pCorr(CMi,Si(Yi)), which of the following regressions will be run:

Accepted Answer

A)  CMi = b0 + b1Si + ei 
B)  CMi = b0 + b1Yi + ei 
C)  Si = b0 + b1CMi + ei 
D)  Si = b0 + b1Yi + ei 
A)  CMi = b0 + b1Si + ei 
B)  CMi = b0 + b1Yi + ei 
C)  Si = b0 + b1CMi + ei 
D)  Si = b0 + b1Yi + ei

Question 7

Which of the following settings best describes a setting in which one would be making a passive prediction?

Accepted Answer

A)  Predicting the rise in sales following an increase in the promotional activities of your firm. 
B)  Predicting the increase in labor productivity following the rollout of mandatory HR workshop. 
C)  Predicting vehicle sales as a function of the daily max temperature and total rainfall. 
D)  Predicting the change in click-through rates following the change in banner size. 
A)  Predicting the rise in sales following an increase in the promotional activities of your firm. 
B)  Predicting the increase in labor productivity following the rollout of mandatory HR workshop. 
C)  Predicting vehicle sales as a function of the daily max temperature and total rainfall. 
D)  Predicting the change in click-through rates following the change in banner size.

Question 8

After estimating a regression of your firm's store sales and the number of local competitors as follows: Sales = 321,752 + 70.35 Number of Competitors. You are willing to believe you have a random sample of store locations, and a sufficiently large sample size. What is wrong with the following logic, "We need more competitors to enter the markets we're in, so that our sales will rise?"

Accepted Answer

A)  The positive coefficient on number of competitors is not a causal estimate. 
B)  The positive coefficient on number of competitors isn't a consistent estimate. 
C)  The number of competitors might be measured with error. 
D)  You're making an active prediction. 
A)  The positive coefficient on number of competitors is not a causal estimate. 
B)  The positive coefficient on number of competitors isn't a consistent estimate. 
C)  The number of competitors might be measured with error. 
D)  You're making an active prediction.

Question 9

In characterizing determining functions, the "error term" represents:

Accepted Answer

A)  unobserved factors that determine the outcome. 
B)  factors that correlate with the treatment. 
C)  deviations from random sampling. 
D)  deviations from random treatment assignment. 
A)  unobserved factors that determine the outcome. 
B)  factors that correlate with the treatment. 
C)  deviations from random sampling. 
D)  deviations from random treatment assignment.

Question 10

The total sum of squares is given by the sum of:

Accepted Answer

A)  the squares for the outcome (or dependent variable of your model). 
B)  squares for the treatment (or independent variable of your model). 
C)  squared residuals. 
D)  the squared difference between each observation Y and the average value for Y. 
A)  the squares for the outcome (or dependent variable of your model). 
B)  squares for the treatment (or independent variable of your model). 
C)  squared residuals. 
D)  the squared difference between each observation Y and the average value for Y.

Question 11

The distinction between causality and correlation is best described as:

Accepted Answer

A)  correlation implies a change in one variable creates a change in another, causality measures co-movement. 
B)  causality implies positive amounts of co-movement, correlation implies negative amounts of co-movement. 
C)  causality implies a change in one variable creates a change in another, correlation implies variables move together. 
D)  None of the answers is correct. 
A)  correlation implies a change in one variable creates a change in another, causality measures co-movement. 
B)  causality implies positive amounts of co-movement, correlation implies negative amounts of co-movement. 
C)  causality implies a change in one variable creates a change in another, correlation implies variables move together. 
D)  None of the answers is correct.

Question 12

In the determining function for Sales given by Salesi = α0 + α1Pricei + Ui, what is the role of Ui?

Accepted Answer

A)  Factors other than price that impact sales 
B)  Factors correlated with price that do not impact sales 
C)  Factors uncorrelated with price that do not impact sales 
D)  Pure measurement error in price 
A)  Factors other than price that impact sales 
B)  Factors correlated with price that do not impact sales 
C)  Factors uncorrelated with price that do not impact sales 
D)  Pure measurement error in price

What are the two key assumptions necessary to establish causality from a regression model?

In the following determining function, Earnings_i = α₀ + α₁Education_i + U_i, what might be a factor contained in U_i?

In the determining function for Labor productivity given by Labor Prod_i = α₀ + α₁Age_i + α₂Education_i + U_i, what is the role of U_i?

Which of the following formulas is the correct calculation for the unconditional correlation between variables X and Y?

To calculate the semi-partial correlation of Coconut Milk household purchases CM_i and Salami Deli Meat household purchases S_i controlling for household income Y_i, (i.e., pCorr(CM_i,S_i(Y_i)), which of the following regressions will be run:

Which of the following settings best describes a setting in which one would be making a passive prediction?

In characterizing determining functions, the "error term" represents:

The total sum of squares is given by the sum of:

The distinction between causality and correlation is best described as:

In the determining function for Sales given by Sales_i = α₀ + α₁Price_i + U_i, what is the role of U_i?

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

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters

Exam 6: Correlation Vs Causality in Regression Analysis

What are the two key assumptions necessary to establish causality from a regression model?

In the following determining function, Earningsi = α0 + α1Educationi + Ui, what might be a factor contained in Ui?

In estimating the causal relationship between Sales and Price in the following determining function Salesi = β0 + β1Pricei + Ui, what assumption in addition to E [Ui ] = 0, justifies that the estimated coefficient on Price can be interpreted as an estimate of the causal effect of Price on Sales?

In the determining function for Labor productivity given by Labor Prodi = α0 + α1Agei + α2Educationi + Ui, what is the role of Ui?

Which of the following formulas is the correct calculation for the unconditional correlation between variables X and Y?

To calculate the semi-partial correlation of Coconut Milk household purchases CMi and Salami Deli Meat household purchases Si controlling for household income Yi, (i.e., pCorr(CMi,Si(Yi)), which of the following regressions will be run:

Which of the following settings best describes a setting in which one would be making a passive prediction?

In characterizing determining functions, the "error term" represents:

The total sum of squares is given by the sum of:

The distinction between causality and correlation is best described as:

In the determining function for Sales given by Salesi = α0 + α1Pricei + Ui, what is the role of Ui?

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

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters

In the following determining function, Earnings_i = α₀ + α₁Education_i + U_i, what might be a factor contained in U_i?

In the determining function for Labor productivity given by Labor Prod_i = α₀ + α₁Age_i + α₂Education_i + U_i, what is the role of U_i?

To calculate the semi-partial correlation of Coconut Milk household purchases CM_i and Salami Deli Meat household purchases S_i controlling for household income Y_i, (i.e., pCorr(CM_i,S_i(Y_i)), which of the following regressions will be run:

In the determining function for Sales given by Sales_i = α₀ + α₁Price_i + U_i, what is the role of U_i?