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Consider the following regression model: Weekly Hours = Bo + B1 × Wage¡ + u¿ Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, ß1, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1.

Consider the following regression model: Weekly Hours = Bo + B1 × Wage¡ + u¿ Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, ß1, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1.

Algebra and Trigonometry (MindTap Course List)
Algebra and Trigonometry (MindTap Course List)
4th Edition
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter1: Equations And Graphs
Section1.FOM: Focus On Modeling: Fitting Lines To Data
Problem 10P
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3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage¡ + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that ß1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В) 4. Consider the following regression model: IQi = Bo + B1 × Mozart Hours¡ + ui IQ is the individual's IQ score in elementary school and Mozart Hours is the number of hours the individual spent listening to Mozart up to age 5. A researcher who collects data and regresses IQ against Mozart Hours finds that B1> 0. The OLS estimator, B1, however, likely suffers from omitted variable bias because those individuals who listened to a lot of hours of Mozart during infancy likely come from families with high socioeconomic status. There is a positive association between socioeconomic status and IQ. Because of this omitted variable bias, it is likely the case that ß1_B1. A) В)
Transcribed Image Text:3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage¡ + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that ß1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В) 4. Consider the following regression model: IQi = Bo + B1 × Mozart Hours¡ + ui IQ is the individual's IQ score in elementary school and Mozart Hours is the number of hours the individual spent listening to Mozart up to age 5. A researcher who collects data and regresses IQ against Mozart Hours finds that B1> 0. The OLS estimator, B1, however, likely suffers from omitted variable bias because those individuals who listened to a lot of hours of Mozart during infancy likely come from families with high socioeconomic status. There is a positive association between socioeconomic status and IQ. Because of this omitted variable bias, it is likely the case that ß1_B1. A) В)
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