Here are all the questions and data i need help with: /*================================================================

[answerhappygod]

Here are all the questions and data i need help with:
/*==============================================================================
Create a log file for your results
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/*==============================================================================
The purpose of this exercise is to show how one's looks (i.e.,
beauty) affects hourly wages. The hypothesis is that more beautiful
people earn more. (This is an area of research in labor economics.
See Daniel Hamermesh's work, for example.) Open the beauty.dta data
set. It is a random sample of people in the U.S. and it contains
information about the people's characteristics and wages. An
independent group of people analyzed pictures of every person in
the sample and ranked their "looks" on a scale of 1 to 5, where 5
means most beautiful.
==============================================================================*/
/*==============================================================================
(Q1): Estimate a model where log(wages) are explained by beauty
(looks), years of education (educ), years of experience (exper),
years of experience-squared (expersq), an indicator variable for
female, and a binary variable for black. Use
heteroskedasticty-robut standard errors. Interpret beta1hat and
beta5hat in two sentences and comment on the individual statistical
significance of beta1 and beta5.
==============================================================================*/
/*==============================================================================
(Q2): Is the relationship between experience and log(wages) linear?
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/*==============================================================================
(Q3): Find the marginal effect of another year of experience on
wages when someone has 10 years of experience. Test whether the
marginal effect is statistically significant. Summarize your
answers in a sentence.
==============================================================================*/
/*==============================================================================
(Q4): Create an interaction between looks and female. What is the
marginal effect of another unit of "looks" for females? What is the
marginal effect of another unit of "looks" for males? Evaluate the
statistical significance of each marginal effect.
==============================================================================*/
/*==============================================================================
(Q5): Re-estimate the model in (Q1) but replace the looks variable
with an indicator variable for whether the person has above-average
looks (abvavg) and an indicator for whether the person has
below-average looks (belavg). The omitted cateogry includes people
with average looks. Interpret beta1 and beta2 (coeficients on looks
variables) each in a sentence and comment on their individual
statistical significance.
==============================================================================*/
/*==============================================================================
(Q6): Test the joint statistical significance of the coefficients
on the looks- related variables in the model in (Q3). What do you
conclude?
==============================================================================*/
/*==============================================================================
(Q7) Create an interaction term between below average looks and an
indicator for whether someone works in the service industry. Then
re-estimate the model in (Q5), but add the interaction term and the
indicator variable for whether someone works in a service industry.
Does below-average-looks affect wages differently depending on
whether a person works in a service industry? Please explain.
==============================================================================*/
/*==============================================================================
(Q8) Do you think having below-average looks has a causal effect on
hourly wages? Please explain.
==============================================================================*/
/*==============================================================================
(Q9) Use some combination of variables in the data set to estimate
your own model that is nonlinear in at least one X. Explain why you
chose to estimate the model that you did.
==============================================================================*/
/*==============================================================================
(Q10) Identify one marginal effect for your nonlinear X in
(Q9).
VARIABLES
NAME
LABEL
wage

hourly wage
lwage

log(wage)
belavg

=1 if looks <=2
abvavg

=1 if looks >=4
exper

years of work experience
looks

from 1 to 5
union

= 1 if union member
goodhlth

= 1 if good health
black

= 1 if black
female

= 1 if female
married

= 1 if married
south
= 1 if live in the south
bigcity

= 1 if live in big city
smllcity

= 1 if live in small city
service

= 1 if service industry
expersq

exper 2
educ
years of schooling