> # Weibull regression
> mdata  <-read.table("strike.csv", header=T, sep=",")
> y      <-mdata[,1]
> x      <-mdata[,2]
> status <-mdata[,3]
> mydata <-data.frame(y,x,status)
> library(survival) 
> # AFT model (accelerated failure time model)
> survreg(Surv(y,status)~x, dist="weibull")
Call:
survreg(formula = Surv(y, status) ~ x, dist = "weibull")

Coefficients:
(Intercept)           x 
  3.9663834  -0.6947956 

Scale= 0.9675753 

Loglik(model)= -398.3   Loglik(intercept only)= -398.4
        Chisq= 0.09 on 1 degrees of freedom, p= 0.759 
n= 80 

> p = 1/0.9675753
> beta1 = -(-0.6947956)/0.9675753
> beta0 = -(3.9663834)/0.9675753
> cbind(p, beta0, beta1)
            p     beta0     beta1
[1,] 1.033511 -4.099302 0.7180791

> summary(survreg(Surv(y,status)~x, dist="weibull"))

Call:
survreg(formula = Surv(y, status) ~ x, dist = "weibull")
              Value Std. Error     z      p
(Intercept)  3.9664     0.1448 27.40 <2e-16
x           -0.6948     2.2856 -0.30   0.76
Log(scale)  -0.0330     0.0896 -0.37   0.71

Scale= 0.968 

Weibull distribution
Loglik(model)= -398.3   Loglik(intercept only)= -398.4
        Chisq= 0.09 on 1 degrees of freedom, p= 0.76 
Number of Newton-Raphson Iterations: 7