> # Tobit model using R
> mdata  <-read.table("wage.csv", header=T, sep=",")
> work   <-mdata[,1]
> hour   <-mdata[,2]
> child  <-mdata[,3]
> age    <-mdata[,4]
> edu    <-mdata[,5]
> wage   <-mdata[,6]
> hinc   <-mdata[,7]
> year   <-mdata[,8]
> mydata <-data.frame(work,hour,child,age,edu,wage,hinc,year)
> library(AER)
> mytobit <-tobit(hour ~ edu + year + age + child + hinc, left=0)
> summary(mytobit)

Call:
tobit(formula = hour ~ edu + year + age + child + hinc, left = 0)

Observations:
         Total  Left-censored     Uncensored Right-censored 
           100             50             50              0 

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) 1787.6290  1185.0082   1.509   0.1314    
edu          153.3979    71.4448   2.147   0.0318 *  
year          94.7800    19.8669   4.771 1.84e-06 ***
age          -92.4573    22.7261  -4.068 4.73e-05 ***
child       -913.1790   394.5286  -2.315   0.0206 *  
hinc         -76.4703    39.0119  -1.960   0.0500 *  
Log(scale)     7.1157     0.1099  64.754  < 2e-16 ***
---
Signif. codes:  0 �e***�f 0.001 �e**�f 0.01 �e*�f 0.05 �e.�f 0.1 �e �f 1

Scale: 1231 

Gaussian distribution
Number of Newton-Raphson Iterations: 4 
Log-likelihood: -453.8 on 7 Df
Wald-statistic: 34.61 on 5 Df, p-value: 1.7972e-06