- Given The Following Logarithmic Estimating Equation Log Wage 0 20 64 Log Age Interpret The Coefficient B1 64 1 (41.51 KiB) Viewed 76 times
Given the following estimating equation: wage = 5000-4000 female + 1000age Where wage is annual salary in dollars, female is an indicator variable coded O for male and 1 for female, and age is measure in years. In this equation, what does BO = 5000 mean? O The average salary for females who are zero years old is $5000. The average salary for females at the average age of the sample is $5000. O The average salary for males at the average age of the sample is $5000. The average salary of males who are zero years old is $5000.
Given the following linear probability model: Grad_School = 0.35 + 0.05 study_time - 0.03 party_ti. Where Grad_School is a binary variable coded O for rejection and 1 for acceptance into a graduate program, study_time is hours per week spent studying, and party_time is hours per week spent partying. How should we interpret B2=-0.03? As hours partied per week increases by 1, the probability of getting accepted into graduate school goes down by 0.03 percentage points. O As hours partied per week increases by 1, the probability of getting accepted into graduate school goes down by 3 percentage points O As hours partied per week increases by 1, the number of students getting accepted into graduate school goes down by 5. o As hours partied per week increases by 1, the number of students getting accepted into graduate school goes down by 0.03.
What is the main advantage of using a probit model over an LPM? O A probit model will identify more variables as statistically significant than a comparable LPM. The probabilities predicted by a probit model are always between 0 and 1, while the predictions given by an LPM can go below O or above 1. The probabilities predicted by a probit model can go below O and above 1, where the probabilities predicted by an LPM are always bounded between 0 and 1. O A probit model is easier to interpret than an LPM.