Suppose that the following regression equation best describes the co-movement between Sales, Price and Number of Competitors: Salesi = B + M1Pricei + M2NumCompi. What moment condition would not be used to yield a consistent estimate of B, M1, M2?

A) $\frac { \sum _ { i = 1 } ^ { N } e _ { i } } { N }$ = 0 B) $ \frac{\sum _ { i = 1 } ^ { N } \left( \text { Sales } _ { i } - b - m _ { 1 } \text { Price } _ { i } - m _ { 2 } \text { Num Comp } _ { i } \right) \text { Price } _ { i }}{N} $ = 0 C) $ \frac{\sum _ { i = 1 } ^ { N } \left( \text { Sales } _ { i } - b - m _ { 1 } \text { Price } _ { i } - m _ { 2 } \text { NumComp } _ { i } \right)}{N} $ = 0 D) $ \frac{\sum _ { i = 1 } ^ { N } \left( \text { Sales } _ { i } - b - m _ { 1 } \text { Price } _ { i } \right) \text { Price } _ { i }}{N} $ = 0 A) $\frac { \sum _ { i = 1 } ^ { N } e _ { i } } { N }$ = 0 B) $ \frac{\sum _ { i = 1 } ^ { N } \left( \text { Sales } _ { i } - b - m _ { 1 } \text { Price } _ { i } - m _ { 2 } \text { Num Comp } _ { i } \right) \text { Price } _ { i }}{N} $ = 0 C) $ \frac{\sum _ { i = 1 } ^ { N } \left( \text { Sales } _ { i } - b - m _ { 1 } \text { Price } _ { i } - m _ { 2 } \text { NumComp } _ { i } \right)}{N} $ = 0 D) $ \frac{\sum _ { i = 1 } ^ { N } \left( \text { Sales } _ { i } - b - m _ { 1 } \text { Price } _ { i } \right) \text { Price } _ { i }}{N} $ = 0 D

If the determining function for Sales is given by Salesi = α0 + α1Pricei + Ui, what will the correlation between Sales and Price be?

A) α0 + α1 B) α1 C) Cor(Pricei, Ui) D) There is not enough information. A) α0 + α1 B) α1 C) Cor(Pricei, Ui) D) There is not enough information.

If the t-stat for the sample estimate of a coefficient, M1 calculated as the following, t = $\frac { m _ { 1 } - 1 } { S _ { m _ { 1 } } }$ , where m1 is the estimated coefficient and S $m _ { 1 }$ is the properly estimated standard error for the coefficient, comes out to be 2.7, what is the appropriate conclusion?

A) Reject the null hypothesis that the population coefficient M1 = 1 at a 99% confidence level. B) Fail to reject the null hypothesis that the population coefficient M1 = 1 at a 95% confidence level. C) Reject the null hypothesis that the population coefficient M1 = 2 at a 99% confidence level. D) None of these choices are correct. A) Reject the null hypothesis that the population coefficient M1 = 1 at a 99% confidence level. B) Fail to reject the null hypothesis that the population coefficient M1 = 1 at a 95% confidence level. C) Reject the null hypothesis that the population coefficient M1 = 2 at a 99% confidence level. D) None of these choices are correct.

The part of the outcome that we can explicitly determine, fi (X1i, X2i, ..., Xki), is known as the:

A) unbiased estimate. B) determining function. C) covariance. D) partial correlation. A) unbiased estimate. B) determining function. C) covariance. D) partial correlation.

Exam 6: Correlation Vs Causality in Regression Analysis

Suppose you estimate the following regression of a firm's Sales and number of employees at each location across the country: Sales = 95,342 + 0.76 Number of Employees. You are willing to believe you have a random sample of store locations, and a sufficiently large sample size. Which statements are not yet justified by the regression results?

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Question 1

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Consider the following proposed determining function for a student's grade on the econometrics final, FinalGrade_i = 68 + 4 HoursStudied_i - 2 Num Other Finals_i + U_i, where hours studied is the number of hours studied during finals week by student i, and number of other finals is the number of other finals student i has during finals week. Derive the formula for the change in a student's final grade with respect to a unit change in the number of other finals the student has in finals week.

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Question 2

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Suppose that the following regression equation best describes the co-movement between Sales, Price and Number of Competitors: Sales_i = B + M₁Price_i + M₂NumComp_i. What moment condition would not be used to yield a consistent estimate of B, M₁, M₂?

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Question 3

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After estimating a regression of each employee's number of contracts sold and their tenure (in number of years) at the company as follows: Contracts = 30.5 + 4.5 Tenure. You are willing to believe you have a random sample of employees, and a sufficiently large sample size. Given these results, is it appropriate to make the following claim, "Our more tenured employees at the company on average get awarded more contracts"?

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Question 4

If you are comfortable with assumptions required for causal analysis and you have estimated the relationship between Sales and running a promotion together with a price discount to be Sales_i = 140.3(60) + 4.3(0.7) Promo with standard errors reported in parenthesis. What would you predict to occur in the event of running a discount next week?

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Question 5

If the determining function for Sales is given by Sales_i = α₀ + α₁Price_i + U_i, what will the correlation between Sales and Price be?

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Question 6

If the t-stat for the sample estimate of a coefficient, M₁ calculated as the following, t = $\frac { m _ { 1 } - 1 } { S _ { m _ { 1 } } }$ , where m₁ is the estimated coefficient and S $m _ { 1 }$ is the properly estimated standard error for the coefficient, comes out to be 2.7, what is the appropriate conclusion?

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Question 7

The assumption of no correlation between the error term (U) and treatment(s) (X) is similar to the assumption of what aspect used in the scientific method?

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Question 8

When making passive predictions, which type of relationship must you believe continues to hold when you move your estimation sample to your forecasting sample?

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Question 9

Suppose the regression of Output per Hour on employee Age yields a coefficient of -4.3 with a standard error of 1.2. Which of the following equations would properly report the 95% confidence interval for the population coefficient?

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Question 10

If the residuals of a regression model, Y_i= B + MX_i + E_i, are such that their variances vary across all values of X, then they are said to be:

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Question 11

In calculating the partial correlation between two variables holding one other variable constant, how many sets of residuals will be created?

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Question 12

If the determining function for the likelihood of a mortgage application receiving a loan is given by, Loan Successful = 0.60 + 0.01 Credit Score. What would be the causal effect of increasing your credit score by 10 points?

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Question 13

In making passive predictions, it is usually sufficient to (consistently) estimate what sort of relationships?

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Question 14

A consistent estimator is an estimator whose:

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Question 15

Which of the following conditions ensures that the estimates of the coefficients for the population regression equation are distributed normally?

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Question 16

If the residuals of a regression model, Y_i= B + MX_i+ E_i, satisfy the condition that their variances are constant across all values of X, then they are said to be:

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Question 17

Suppose you've run a regression relating Revenues to TV Ads and Online Ads. You are willing to make the necessary assumptions to deduce causality and run hypothesis tests. Your results are as follows: Coefficients Stardard Error t-Stat P-value Iratercept 5988.043107 2202.136765 2.719196738 0.006881952 IV Ads 199.6320212 27.82658673 7.174146912 4.63154-12 Orline Ads 53.52092429 41.0818115 1.302788809 0.193533758 If you tested the null hypothesis that Online Ads have no impact on Revenues at the 90% confidence level (i.e., 90% degree of support), you would:

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Question 18

The part of the outcome that we can explicitly determine, f_i (X₁_i, X₂_i, ..., X_ki), is known as the:

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Question 19

Why can't the regression coefficients in correlation analysis be interpreted as the causal effect of a treatment on the outcome?

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Question 20

Showing 1 - 20 of 52

Suppose that the following regression equation best describes the co-movement between Sales, Price and Number of Competitors: Sales_i = B + M₁Price_i + M₂NumComp_i. What moment condition would not be used to yield a consistent estimate of B, M₁, M₂?

If the determining function for Sales is given by Sales_i = α₀ + α₁Price_i + U_i, what will the correlation between Sales and Price be?

The assumption of no correlation between the error term (U) and treatment(s) (X) is similar to the assumption of what aspect used in the scientific method?

When making passive predictions, which type of relationship must you believe continues to hold when you move your estimation sample to your forecasting sample?

Suppose the regression of Output per Hour on employee Age yields a coefficient of -4.3 with a standard error of 1.2. Which of the following equations would properly report the 95% confidence interval for the population coefficient?

If the residuals of a regression model, Y_i= B + MX_i + E_i, are such that their variances vary across all values of X, then they are said to be:

In calculating the partial correlation between two variables holding one other variable constant, how many sets of residuals will be created?

If the determining function for the likelihood of a mortgage application receiving a loan is given by, Loan Successful = 0.60 + 0.01 Credit Score. What would be the causal effect of increasing your credit score by 10 points?

In making passive predictions, it is usually sufficient to (consistently) estimate what sort of relationships?

A consistent estimator is an estimator whose:

Which of the following conditions ensures that the estimates of the coefficients for the population regression equation are distributed normally?

If the residuals of a regression model, Y_i= B + MX_i+ E_i, satisfy the condition that their variances are constant across all values of X, then they are said to be:

The part of the outcome that we can explicitly determine, f_i (X₁_i, X₂_i, ..., X_ki), is known as the:

Why can't the regression coefficients in correlation analysis be interpreted as the causal effect of a treatment on the outcome?

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

Suppose that the following regression equation best describes the co-movement between Sales, Price and Number of Competitors: Salesi = B + M1Pricei + M2NumCompi. What moment condition would not be used to yield a consistent estimate of B, M1, M2?

If the determining function for Sales is given by Salesi = α0 + α1Pricei + Ui, what will the correlation between Sales and Price be?

The assumption of no correlation between the error term (U) and treatment(s) (X) is similar to the assumption of what aspect used in the scientific method?

When making passive predictions, which type of relationship must you believe continues to hold when you move your estimation sample to your forecasting sample?

Suppose the regression of Output per Hour on employee Age yields a coefficient of -4.3 with a standard error of 1.2. Which of the following equations would properly report the 95% confidence interval for the population coefficient?

If the residuals of a regression model, Yi = B + MXi + Ei, are such that their variances vary across all values of X, then they are said to be:

In calculating the partial correlation between two variables holding one other variable constant, how many sets of residuals will be created?

If the determining function for the likelihood of a mortgage application receiving a loan is given by, Loan Successful = 0.60 + 0.01 Credit Score. What would be the causal effect of increasing your credit score by 10 points?

In making passive predictions, it is usually sufficient to (consistently) estimate what sort of relationships?

A consistent estimator is an estimator whose:

Which of the following conditions ensures that the estimates of the coefficients for the population regression equation are distributed normally?

If the residuals of a regression model, Yi = B + MXi + Ei, satisfy the condition that their variances are constant across all values of X, then they are said to be:

The part of the outcome that we can explicitly determine, fi (X1i, X2i, ..., Xki), is known as the:

Why can't the regression coefficients in correlation analysis be interpreted as the causal effect of a treatment on the outcome?

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

Suppose that the following regression equation best describes the co-movement between Sales, Price and Number of Competitors: Sales_i = B + M₁Price_i + M₂NumComp_i. What moment condition would not be used to yield a consistent estimate of B, M₁, M₂?

If the determining function for Sales is given by Sales_i = α₀ + α₁Price_i + U_i, what will the correlation between Sales and Price be?

If the residuals of a regression model, Y_i= B + MX_i + E_i, are such that their variances vary across all values of X, then they are said to be:

If the residuals of a regression model, Y_i= B + MX_i+ E_i, satisfy the condition that their variances are constant across all values of X, then they are said to be:

The part of the outcome that we can explicitly determine, f_i (X₁_i, X₂_i, ..., X_ki), is known as the: