These data are from the Data and Story Library (DASL) hosted at Carnegie Mellon University
(http://lib.stat.cmu.edu/DASL/Stories/USCrime.html)

These data are crime-related and demographic statistics for 47 US states in 1960. The data were 
collected from the FBI's Uniform Crime Report and other government agencies to determine how the 
dependent variable crime rate (R) depends on the other variables measured in the study.

The goal is to fit a linear regression model to predict crime rate (R) from the other variables.  
First fit a model with all of the covariates.  Create residual and leverage plots for that model, 
and calculate VIF values.  With this information, work toward producing a final model to predict 
crime rate that addresses any problems that you observe from the initial model with all variables.

Variable Names:

   1. R: Crime rate: # of offenses reported to police per million population
   2. Age: The number of males of age 14-24 per 1000 population
   3. S: Indicator variable for Southern states (0 = No, 1 = Yes)
   4. Ed: Mean # of years of schooling x 10 for persons of age 25 or older
   5. Ex0: 1960 per capita expenditure on police by state and local government
   6. Ex1: 1959 per capita expenditure on police by state and local government
   7. LF: Labor force participation rate per 1000 civilian urban males age 14-24
   8. M: The number of males per 1000 females
   9. N: State population size in hundred thousands
  10. NW: The number of non-whites per 1000 population
  11. U1: Unemployment rate of urban males per 1000 of age 14-24
  12. U2: Unemployment rate of urban males per 1000 of age 35-39
  13. W: Median value of transferable goods and assets or family income in tens of $
  14. X: The number of families per 1000 earning below 1/2 the median income