Description
• Questions 1 and 2 ask you to use Stata. Do all data manipulation and analyses
in separate .do files named ps2_q1.do and ps2_q2.do.
• ps2_q1.do should write a comma delimited file recs2015_usage.csv with
the requested point estimates and standard errors.
• Run ps2_q2.do in batch mode and produce a ps2_q2.log file with the
output. Output in the log file should be clearly labeled and referred to in your
typed answer to the questions.
• Question 3 asks you to analyze data in R. You should submit your code for this
problem as ps2_q3.R.
• You should submit a single compressed archive (.zip) which contains the
following files:
◦ ps2.pdf or ps2.html
◦ ps2.Rmd
◦ ps2_q1.do
◦ recs2015_usage.csv
◦ ps2_q2.do
◦ ps2_q2.log
◦ ps2_q3.R
• All files should be executable without errors.
• All files read, sourced, or referred to within scripts should be assumed to be in
the same working directory (./).
• Your code should be clearly written and it should be possible to assess it by
reading it. Use appropriate variable names and comments. Your style will be
graded using the style rubric [15 points].
• Some of these exercises may require you to use commands or techniques that
were not covered in class or in the course notes. You can use the web as
needed to identify appropriate approaches. Part of the purpose of these
exercises is for you to learn to be resourceful and self sufficient. Questions are
welcome at all times, but please make an attempt to locate relevant information
yourself first.
• You may wish to review:
◦ the tutorial on converting between wide and long data available here,
◦ Richard Williams’s presentation on “Stata’s Margins Command” here.
Question 1 [25 points]
Use Stata to estimate the following national totals for residential energy consumption:
• Electricity usage in kilowatt hours
• Natural gas usage, in hundreds of cubic feet
• Propane usage, in gallons
• Fuel oil or kerosene usage, in gallons
In your analysis, be sure to properly weight the individual observations. Use the
replicate weights to compute standard errors. At the end of your .do file, write the
estimates and standard errors to a delimited file recs2015_usage.csv.
In your .Rmd read recs2015_usage.csv and produce a nicely formatted table with
estimates and 95% confidence intervals.
Question 2 [35 points]
For this question you should use the 2005-2006 NHANES ORAL Health data available
here and the demographic data available here. Your analyses for this question should
be done in Stata, though you may create plots and format tables using R within
Rmarkdown.
For part (b-d), you can ignore the survey aspect of the data and analyze it as if the data
were a simple random sample.
• [5 points] Determine how to read both data sets into Stata and merge them
together by the participant id SEQN.
• [5 points] Use logistic regression to estimate the relationship between age (in
months) and the probability that an individual has a primary rather than a
missing or permanent upper right 2nd bicuspid. You can recode permanent root
fragments as permanent and drop individuals for whom this tooth was not
assessed. Use the fitted model to estimate the ages at which 25, 50, and 75%
of individuals lose their primary upper right 2nd bicuspid. Round these to the
nearest month. Choose a range of representative age values with one year
increments by taking the floor (in years) of the 25%-ile and the ceiling (in years)
of the 75%-ile.
• [10 points] In the regression above, control for demographics in the following
way:
◦ Add gender to the model and retain it if it improves the BIC.
◦ Create indicators for each race/ethnicity category using the largest as the
reference and collapsing ‘Other Hispanic’ and ‘Other’. In order of group
size in the sample, add each category retaining those that improve BIC.
◦ Add poverty income ratio to the model and retain it if it improves BIC.
In your pdf document, include a nicely formatted regression table for the final model
and an explanation of the model fitting process.
• [10 points] Use the margins command to compute:
1. Adjusted predctions at the mean (for other values) at each of the
representative ages determined in part b.
2. The marginal effects at the mean of any retained categorical variables at
the same representative ages.
3. The average marginal effect of any retained categorical varialbes at the
representative ages.
• [5 points] Refit your final model from part c using svy and comment on the
differences. Include a nicely formatted regression table and cite evidence to
justify your comments.
You should use the following command to set up the survey weights:
svyset sdmvpsu [pweight=wtmec2yr], strata(sdmvstra)
vce(linearized)
Question 3 [30 points]
Repeat part a-d of question 2 using R. For part d, you may either use the “margins”
package or code the computations yourself.
