Data
====

1. grades.data.R
  - N      : number of observations
  - final  : final exam scores
  - midterm: midterm exam scores

2. lightspeed.data.R
  - N: number of observations
  - y: parameter to estimate speed of light

3. roaches.data.R
  - N	   : number of observations
  - exposure2: number of trap-days
  - roach1   : 
  - senior   : is senior building for elders? 1: Yes, 0: No
  - treatment: received treatment? 1: Yes, 0: No
  - y	     : number of roaches caught in a set of traps

4. unemployment.data.R
  - N    : number of observations
  - y    : current unemployment rate
  - y_lag: last year's unemployment rate

Models
======

1. Zero predictors
  lightspeed.stan: lm(y ~ 1)

2. One predictor
  grades.stan      : lm(final ~ midterm)
  unemployment.stan: lm(y ~ y_lag)
  y_x.stan	 : lm(y ~ x)

3. Poisson regression with exposure
  roaches.stan: glm (y ~ roach1 + treatment + senior, family=poisson, 
  		     offset=log(exposure2))

4. Poisson overdispersion regression
  roaches_overdispersion.stan: glm(y ~ roach1 + treatment + senior, 
				   family = quasipoisson, 
				   offset = log(exposure2))