model
{
#   (Regression_106_Example.txt)
#
#  Y[1] = Bush Vote by CD in 2000
#
#  X[,1] = 1 if South (CQ def.), 0 otherwise
#  X[,2] = Percent Black in CD
#  X[,3] = Percent Hispanic in CD
#  X[,4] = Family income in Thousands of Dollars
#  X[,5] = DW-NOMINATE 1st Dimension
#  X[,6] = DW-NOMINATE 2nd Dimension
#  
#  PRIORS
#
         tau ~ dgamma(1.0E-1, 1.0E-1)
         for (k in 1 : K) { beta[k] ~ dnorm(0,0.001)} # vague priors
#	  
# LIKELIHOOD	
#
    for (i in 1 : N)  # loop over CDs
    {     
         V[i,1] ~ dnorm( mu[i] , tau)
         mu[i] <- beta[1]+V[i,2]*beta[2]+V[i,3]*beta[3]+V[i,4]*beta[4]+V[i,5]*beta[5]+V[i,6]*beta[6]+V[i,7]*beta[7]
    }  
    sigma <- sqrt(1/tau)
 }