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.