model {
for (i in 1:23) {
y[i] ~ dt(mu[i], tau, dof)
mu[i] <- beta0 + beta[1]*MAN[i]
}
beta0 ~ dnorm(0, 0.0001)
beta[1] ~ dnorm(0, 0.0001)
tau <- 1/pow(sigma, 2)
sigma ~ dunif(0, 100)
sd <- sigma*sqrt(dof/(dof-2))

dummy <- AUTO[1] + DIST[1]
}

Inits:
list(beta0 = 0, beta = c(0), sigma = 1)

Data:
list(dof = 4)

Data:
MAN[] AUTO[] y[] DIST[]
-15.76 1.09 3.19 1
0.98 0.62 -3.45 1
3.71 0.61 0.04 1
-5.37 -1.01 6.62 1
-10.23 -0.76 3.61 1
-8.32 1.91 2.67 1
-7.80 0.40 -2.45 1
6.77 -1.71 9.31 1
-8.81 -0.76 15.29 1
-9.56 -1.34 3.68 1
-2.06 -1.71 8.63 2
-0.76 -1.82 10.82 2
-6.30 -4.91 -0.50 2
39.40 -2.65 -11.00 2
-10.79 0.11 2.05 2
-8.16 0.52 11.80 2
-2.82 -2.54 -2.02 2
-16.19 -0.07 0.94 3
-11.00 -0.83 4.42 3
-14.60 0.98 -0.86 3
-17.96 -3.41 -0.92 3
0.76 2.97 2.61 3
-10.77 2.92 1.58 3
END

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   beta[1]   -0.1244   0.1449   0.002557   -0.358   -0.1455   0.2097   5001   10000
   beta0   1.699   1.632   0.02879   -1.191   1.59   5.222   5001   10000
   sd   6.905   1.463   0.02551   4.619   6.726   10.2   5001   10000
   sigma   4.883   1.035   0.01803   3.266   4.756   7.214   5001   10000