Suppose you've run a regression relating log(Output) to log(Worker Hours) in Excel. You are willing to make the necessary assumptions to deduce causality and run hypothesis tests. Your results are as follows: \[\begin{array} { l c c c c } & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & P \text {-value } \\ \hline \text { Irtercept } & 17.4583044 & 28.47075584 & 0.613201297 & 0.541240031 \\ \text { Log(Worker Hours) } & 2.699483287 & 0.711729096 & 3.792852225 & 0.000264681 \end{array}\] How should you interpret the coefficient on Log(Worker Hours) of 2.69?

A) A 1% increase in Worker Hours leads to a 2.69% increase in Output. B) A 1 unit increase in Worker Hours leads to a 2.69% increase in Output. C) A 1% increase in Worker Hours leads to a .0269% increase in Output. D) A 2.69% increase in Worker Hours leads to a 1% increase in Output. A) A 1% increase in Worker Hours leads to a 2.69% increase in Output. B) A 1 unit increase in Worker Hours leads to a 2.69% increase in Output. C) A 1% increase in Worker Hours leads to a .0269% increase in Output. D) A 2.69% increase in Worker Hours leads to a 1% increase in Output.

If the decision to include average tenure at the company as a control variable in an attempt to estimate if West coast store locations are outperforming store locations in the rest of the country (i.e., the regression, StoreProfitsi = β0 + β1West Coasti + β2Avg.Tenurei + Ui), which of the following conditions need to be true in order for the estimate of β1 to be the same regardless of whether Avg. Tenure is used as a control or not?

A) The p-value on average tenure is low. B) The p-value on average tenure is above 0.10 C) Average tenure and West Coast are uncorrelated. D) The semi-partial correlation of Store Profits on West Coast holding Avg. Tenure fixed is zero. A) The p-value on average tenure is low. B) The p-value on average tenure is above 0.10 C) Average tenure and West Coast are uncorrelated. D) The semi-partial correlation of Store Profits on West Coast holding Avg. Tenure fixed is zero.

Exam 7: Basic Methods for Establishing Causal Inference

Suppose you know the determining function for what drives restaurant sales is of the following form WeeklySales_i = β₀ + β₁WeeklyPromoSpend_i + β₂HolidayWeek_i + U_i. Suppose further that your client told you that she is spending $1,000 per week on weekly promotions, except during the summer where she doubles it. Which statement best describes the role of "summer weeks" in estimating the causal effect of weekly promotion spent using the determining function above?

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Question 1

Correct Answer:

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Can we estimate the following equation using standard linear regression techniques: Y_i = β₀ + β₁X_i + β₂X_i² + U_i?

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(Multiple Choice)

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Question 2

Correct Answer:

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A selected sample is one that is:

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(Multiple Choice)

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Question 3

Correct Answer:

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A control variable is a variable included in a regression equation whose purpose is to:

(Multiple Choice)

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Question 4

Your first candidate for a regression to identify the effect of X₁ on Y is: Y_i = β₀ + β₁X₁_i + β₂X₂_i + U_i, where X₂ is a control variable. Suppose a member of your consulting team suggests to you that another variable R_i should be a control that is also included in your regression, in order for you not to be worried about the endogeneity problem. What condition could you credibly test using data on Y, X₁, X₂, and R which could justify the inclusion of R_i as a control in your regression?

(Multiple Choice)

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Question 5

Suppose in an attempt to estimate the effect of how listing with a real estate agent impacts a house's selling price you estimate the following regression: Price_i = 10.2 (3.2) + 2.3 (0.8) Agent + -2.9 (0.3) Distance to Downtown_i, where Distance to Downtown is a proxy variable, which can be used to control for the desirability of the location, and the standard errors for each coefficient are reported in parenthesis. How should we interpret the regression results for the coefficient on Agent?

(Multiple Choice)

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Question 6

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

(Multiple Choice)

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Question 7

Using a proxy variable can be appropriate in all of the following settings except estimating a:

(Multiple Choice)

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Question 8

If you are using number of competitors as a control variable and the local unemployment rate as a proxy for economic climate in a regression to estimate the effect of price on sales, all the following variables will show up in the regression except for:

(Multiple Choice)

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Question 9

Sometimes including independent variables in a regression serve as a "data sanity check," in so much as they facilitate a:

(Multiple Choice)

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Question 10

The presence of a confounding factor will lead to failure of which of the critical assumptions used to justify claiming your estimates consistently estimate a causal effect?

(Multiple Choice)

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Question 11

Suppose the relationship between temperature and demand for electricity is non-linear, due to the high demand for electricity when it is both very cold and very hot outside. What is the risk of estimating the effect of price on demand for electricity by the regression equation: Electricity Consumption_t = α₀ + α₁Price_t + α₂Temperature_t + U_t?

(Multiple Choice)

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Question 12

Suppose you've run a regression relating log(Output) to log(Worker Hours) in Excel. You are willing to make the necessary assumptions to deduce causality and run hypothesis tests. Your results are as follows: Coefficients Standard Error t Stat P -value Irtercept 17.4583044 28.47075584 0.613201297 0.541240031 Log(Worker Hours) 2.699483287 0.711729096 3.792852225 0.000264681 How should you interpret the coefficient on Log(Worker Hours) of 2.69?

(Multiple Choice)

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Question 13

The determining function that drives share of accepted job offers for a company is given by the following equation: AcceptedOffers_t = α₀ + α₁StartingSalary_t + α₂EconomicClimate_t + U_t, where the unit of observation is particular month (t). Suppose one wanted to use the national unemployment rate (unemployment_t) as a proxy for EconomicClimate (i.e., ran the above regression replacing the UnemploymentRate with EconomicClimate). How should we interpret the estimated coefficient on the UnemploymentRate (i.e., the proxy variable)?

(Multiple Choice)

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Question 14

If the decision to include average tenure at the company as a control variable in an attempt to estimate if West coast store locations are outperforming store locations in the rest of the country (i.e., the regression, StoreProfits_i = β₀ + β₁West Coast_i + β₂Avg.Tenure_i + U_i), which of the following conditions need to be true in order for the estimate of β₁ to be the same regardless of whether Avg. Tenure is used as a control or not?

(Multiple Choice)

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Question 15

Dropping irrelevant variables from a regression equation might provide a better regression in what sense?

(Multiple Choice)

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Question 16

Assuming that you are trying to determine the true effect of how years of education affect career earnings (Earnings_i = β₀ + β₁Education_i + U_i), and that this effect is positive . What would be the likely induced correlation between Education and U_i of receiving a sample that only had individuals that made over $85,000?

(Multiple Choice)

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Question 17

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. Suppose also that you only observe values of Y that are above 50. What is the consequence of this selection on the values of Y?

(Multiple Choice)

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Question 18

What are the two primary criteria for identifying "good" controls?

(Multiple Choice)

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Question 19

Why is the use of polynomial functional forms typical in trying to estimate non-linear functional forms?

(Multiple Choice)

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Question 20

Showing 1 - 20 of 49

Can we estimate the following equation using standard linear regression techniques: Y_i = β₀ + β₁X_i + β₂X_i² + U_i?

A selected sample is one that is:

A control variable is a variable included in a regression equation whose purpose is to:

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

Using a proxy variable can be appropriate in all of the following settings except estimating a:

If you are using number of competitors as a control variable and the local unemployment rate as a proxy for economic climate in a regression to estimate the effect of price on sales, all the following variables will show up in the regression except for:

Sometimes including independent variables in a regression serve as a "data sanity check," in so much as they facilitate a:

The presence of a confounding factor will lead to failure of which of the critical assumptions used to justify claiming your estimates consistently estimate a causal effect?

Dropping irrelevant variables from a regression equation might provide a better regression in what sense?

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. Suppose also that you only observe values of Y that are above 50. What is the consequence of this selection on the values of Y?

What are the two primary criteria for identifying "good" controls?

Why is the use of polynomial functional forms typical in trying to estimate non-linear functional forms?

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

Can we estimate the following equation using standard linear regression techniques: Yi = β0 + β1Xi + β2Xi2 + Ui?

A selected sample is one that is:

A control variable is a variable included in a regression equation whose purpose is to:

Suppose you are estimating the following model: Yi = β0 + β1Xi + Ui. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

Using a proxy variable can be appropriate in all of the following settings except estimating a:

If you are using number of competitors as a control variable and the local unemployment rate as a proxy for economic climate in a regression to estimate the effect of price on sales, all the following variables will show up in the regression except for:

Sometimes including independent variables in a regression serve as a "data sanity check," in so much as they facilitate a:

The presence of a confounding factor will lead to failure of which of the critical assumptions used to justify claiming your estimates consistently estimate a causal effect?

Dropping irrelevant variables from a regression equation might provide a better regression in what sense?

Suppose you are estimating the following model: Yi = β0 + β1Xi + Ui. Suppose also that you only observe values of Y that are above 50. What is the consequence of this selection on the values of Y?

What are the two primary criteria for identifying "good" controls?

Why is the use of polynomial functional forms typical in trying to estimate non-linear functional forms?

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

Can we estimate the following equation using standard linear regression techniques: Y_i = β₀ + β₁X_i + β₂X_i² + U_i?

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. Suppose also that you only observe values of Y that are above 50. What is the consequence of this selection on the values of Y?