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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
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 4GP
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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
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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