1. Use the **crime** data file that was used in chapter 2 (use
http://www.ats.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 http://www.ats.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 http://www.ats.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 http://www.ats.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 http://www.ats.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**.