Suppose that you have many observations of the employee level longevity and the wage earned by that employee that year. You run the regression Longevityi = β0 + β1 Wagei +Ui, and get an estimate of β1. Now, suppose that a member of the analytics team suggests that education is an omitted variable in your regression and is likely biasing your estimate of β1. Suppose you knew that, conditional on Wage, more educated employees tended to have shorter stints with the company (lower longevity) and that the error, ηi, in the equation Longevityi = β0 + β1Wagei + β2 Educationi + Ui, was uncorrelated with both Wage and Education. How would you sign the bias on your estimate of β1?

A) Argue that education is positively correlated with wage and that your estimate of β1 is an upper bound. B) Argue that education is negatively correlated with wage and that your estimate of β1 is a lower bound. C) Argue that education is positively correlated with wage and that your estimate of β1 is a lower bound. D) Argue that education is positively correlated with longevity and that your estimate of β1 is an upper bound. A) Argue that education is positively correlated with wage and that your estimate of β1 is an upper bound. B) Argue that education is negatively correlated with wage and that your estimate of β1 is a lower bound. C) Argue that education is positively correlated with wage and that your estimate of β1 is a lower bound. D) Argue that education is positively correlated with longevity and that your estimate of β1 is an upper bound.

Exam 10: Identification and Data Assessment

If one was to include in a regression a binary dummy variable for all four regions of the country (East, West, South, North) - what identification challenge would be presented?

(Multiple Choice)

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Question 41

If one suffers from the endogeneity problem for identification, it will result in your coefficient estimates being:

(Multiple Choice)

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Question 42

What condition best describes the endogeneity problem?

(Multiple Choice)

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Question 43

For a parameter that is identified, what is the role of having a larger sample size?

(Multiple Choice)

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Question 44

Suppose that you have many observations of the employee level longevity and the wage earned by that employee that year. You run the regression Longevity_i = β₀ + β₁ Wage_i +U_i, and get an estimate of β₁. Now, suppose that a member of the analytics team suggests that education is an omitted variable in your regression and is likely biasing your estimate of β₁. Suppose you knew that, conditional on Wage, more educated employees tended to have shorter stints with the company (lower longevity) and that the error, η_i, in the equation Longevity_i = β₀ + β₁Wage_i + β₂ Education_i + U_i, was uncorrelated with both Wage and Education. How would you sign the bias on your estimate of β₁?

(Multiple Choice)

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Question 45

When functional form choice is used to alleviate an interpolation/extrapolation identification challenge the justification represents what sort of element in our data reasoning framework?

(Multiple Choice)

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Question 46

Which of the following is a step that can remedy an interpolation/extrapolation identification problem?

(Multiple Choice)

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Question 47

A usual result from having a model that has imperfect multicollinearity is that:

(Multiple Choice)

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Question 48

When making an interpolation it is possible to use which of the following functional forms?

(Multiple Choice)

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Question 49

Of the remedies for an interpolation/extrapolation identification problem, which requires the most trust in the assumptions surrounding the data generation process?

(Multiple Choice)

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Question 50

Showing 41 - 50 of 50

If one was to include in a regression a binary dummy variable for all four regions of the country (East, West, South, North) - what identification challenge would be presented?

If one suffers from the endogeneity problem for identification, it will result in your coefficient estimates being:

What condition best describes the endogeneity problem?

For a parameter that is identified, what is the role of having a larger sample size?

When functional form choice is used to alleviate an interpolation/extrapolation identification challenge the justification represents what sort of element in our data reasoning framework?

Which of the following is a step that can remedy an interpolation/extrapolation identification problem?

A usual result from having a model that has imperfect multicollinearity is that:

When making an interpolation it is possible to use which of the following functional forms?

Of the remedies for an interpolation/extrapolation identification problem, which requires the most trust in the assumptions surrounding the data generation process?

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

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

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