You are interested in how part-time versus full-time statuss affects the probability of having a pension for men versus women. So, you run a regression with pension coverage -- as the dependent variable, and an indicator for part time status (part), whether someone -- a binary variable identifies as female (female) and the interaction between the two (part_female). pension = 0.6 - 0.2part + 0.05female - 0.1part_female %3D The standard error on "part" is 0.1, on "Female" is 0.025, and on "part_female" is 0.1. If the critical value for a two-tailed test at the 5-percent significance level is 1.96, do full-time working males have significantly different pension coverage than full-time working females?
You are interested in how part-time versus full-time statuss affects the probability of having a pension for men versus women. So, you run a regression with pension coverage -- as the dependent variable, and an indicator for part time status (part), whether someone -- a binary variable identifies as female (female) and the interaction between the two (part_female). pension = 0.6 - 0.2part + 0.05female - 0.1part_female %3D The standard error on "part" is 0.1, on "Female" is 0.025, and on "part_female" is 0.1. If the critical value for a two-tailed test at the 5-percent significance level is 1.96, do full-time working males have significantly different pension coverage than full-time working females?
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 15PPS
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6.
![You are interested in how part-time versus full-time status affects the probability of having a
pension for men versus women. So, you run a regression with pension coverage -- a binary variable
-- as the dependent variable, and an indicator for part time status (part), whether someone
identifies as female (female) and the interaction between the two (part_female).
pension = 0.6 - 0.2part + 0.05female - 0.1part_female
The standard error on "part" is 0.1, on "Female" is 0.025, and on "part_female" is 0.1.
If the critical value for a two-tailed test at the 5-percent significance level is 1.96, do full-time
working males have significantly different pension coverage than full-time working females?
Standard errors in parenthesis apply to coefficients in the order provided.
No
Cannot say from the results provided
Yes](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0f5d3d9d-c31e-4a07-9e40-8111bc442ced%2F00e3b22d-63fd-4d17-b22d-77760a489801%2F0oh9j65_processed.png&w=3840&q=75)
Transcribed Image Text:You are interested in how part-time versus full-time status affects the probability of having a
pension for men versus women. So, you run a regression with pension coverage -- a binary variable
-- as the dependent variable, and an indicator for part time status (part), whether someone
identifies as female (female) and the interaction between the two (part_female).
pension = 0.6 - 0.2part + 0.05female - 0.1part_female
The standard error on "part" is 0.1, on "Female" is 0.025, and on "part_female" is 0.1.
If the critical value for a two-tailed test at the 5-percent significance level is 1.96, do full-time
working males have significantly different pension coverage than full-time working females?
Standard errors in parenthesis apply to coefficients in the order provided.
No
Cannot say from the results provided
Yes
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