1. Use the crime data file that was used in chapter 2 (use
https://stats.idre.ucla.edu/stat/stata/webbooks/reg/crime ) and look at a regression model
predicting murder from pctmetro poverty pcths
and single using OLS and make a avplots and a lvr2plot
following the regression. Are there any states that look worrisome? Repeat this analysis
using regression with robust standard errors and show avplots
for the analysis. Repeat the analysis using robust regression and make a
manually created lvr2plot. Also run the results using qreg.
Compare the results of the different analyses. Look at the weights from the
robust regression and comment on the weights.
2. Using the elemapi2 data file (use https://stats.idre.ucla.edu/stat/stata/webbooks/reg/elemapi2
) pretend that 550 is the lowest score that a school could achieve on api00,
i.e. create a new variable with the api00 score and recode it
such that any score of 550 or below becomes 550. Use meals ell
and emer to predict api scores using 1) OLS to predict the
original api score (before recoding) 2) OLS to predict the recoded score where
550 was the lowest value, and 3) using tobit to predict the
recoded api score indicating the lowest value is 550. Compare the results of
these analyses.
3. Using the elemapi2 data file (use https://stats.idre.ucla.edu/stat/stata/webbooks/reg/elemapi2
) pretend that only schools with api scores of 550 or higher were included in
the sample. Use meals ell and emer
to predict api scores using 1) OLS to predict api from the full set of
observations, 2) OLS to predict api using just the observations with api scores
of 550 or higher, and 3) using truncreg to predict api using
just the observations where api is 550 or higher. Compare the results of these
analyses.
4. Using the hsb2 data file (use https://stats.idre.ucla.edu/stat/stata/webbooks/reg/hsb2
) predict read from science, socst, math and write.
Use the testparm and test commands to test
the equality of the coefficients for science, socst
and math. Use cnsreg to estimate a model where
these three parameters are equal.
5. Using the elemapi2 data file (use https://stats.idre.ucla.edu/stat/stata/webbooks/reg/elemapi2
) consider the following 2 regression equations.
api00 = meals ell emer api99 = meals ell emer
Estimate the coefficients for these predictors in predicting api00
and api99 taking into account the non-independence of the
schools. Test the overall contribution of each of the predictors in jointly
predicting api scores in these two years. Test whether the contribution of emer
is the same for api00 and api99.