Researcher A is interested in the wage differential between genders in the Brisbane area. She interviewed 4,520 full time employees in the Brisbane metropolitan area to collect data on weekly income and gender from each interviewee, along with other individual characteristics that may determine labour market outcomes including, but not limited to, years of schooling, marital status, and number of kids. Every individual in the dataset is either male or female. Researcher A then tried to regress log(income) on the dummy for male and the dummy for female and other individual characteristics including a constant. But, she was confused because the econometrics package refused to compute the estimate for the dummy for male. In fact, the econometrics package omits the dummy for male in order to avoid the problem known as: a. Omitted Variable Bias. b. Perfect Multicollinearity. C. Homoskedastic errors. d. Control variables. e. Existence of Outliers. L
Researcher A is interested in the wage differential between genders in the Brisbane area. She interviewed 4,520 full time employees in the Brisbane metropolitan area to collect data on weekly income and gender from each interviewee, along with other individual characteristics that may determine labour market outcomes including, but not limited to, years of schooling, marital status, and number of kids. Every individual in the dataset is either male or female. Researcher A then tried to regress log(income) on the dummy for male and the dummy for female and other individual characteristics including a constant. But, she was confused because the econometrics package refused to compute the estimate for the dummy for male. In fact, the econometrics package omits the dummy for male in order to avoid the problem known as: a. Omitted Variable Bias. b. Perfect Multicollinearity. C. Homoskedastic errors. d. Control variables. e. Existence of Outliers. L
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Problem 1P
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Question
![Researcher A is interested in the wage differential between genders in the Brisbane area.
She interviewed 4,520 full time employees in the Brisbane metropolitan area to collect
data on weekly income and gender from each interviewee, along with other individual
characteristics that may determine labour market outcomes including, but not limited to,
years of schooling, marital status, and number of kids. Every individual in the dataset is
either male or female.
Researcher A then tried to regress log(income) on the dummy for male and the dummy
for female and other individual characteristics including a constant. But, she was
confused because the econometrics package refused to compute the estimate for the
dummy for male. In fact, the econometrics package omits the dummy for male in order to
avoid the problem known as:
a.
Omitted Variable Bias.
b.
Perfect Multicollinearity.
C.
Homoskedastic errors.
d.
Control variables.
e.
Existence of Outliers.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F1cecaeef-2185-4d64-a3b2-3f78810f9007%2F076c8eca-ea88-4c89-9b30-feda5b920ff7%2Fouh9g1y_processed.png&w=3840&q=75)
Transcribed Image Text:Researcher A is interested in the wage differential between genders in the Brisbane area.
She interviewed 4,520 full time employees in the Brisbane metropolitan area to collect
data on weekly income and gender from each interviewee, along with other individual
characteristics that may determine labour market outcomes including, but not limited to,
years of schooling, marital status, and number of kids. Every individual in the dataset is
either male or female.
Researcher A then tried to regress log(income) on the dummy for male and the dummy
for female and other individual characteristics including a constant. But, she was
confused because the econometrics package refused to compute the estimate for the
dummy for male. In fact, the econometrics package omits the dummy for male in order to
avoid the problem known as:
a.
Omitted Variable Bias.
b.
Perfect Multicollinearity.
C.
Homoskedastic errors.
d.
Control variables.
e.
Existence of Outliers.
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