Comments (0) The problem of coding 0 and 1 in PROC LOGISTIC
PROC LOGISTIC can be used to run logistic regression on a dichotomous dependent variable. Often, these are coded 0 and 1, with 0 for `no’ or the equivalent, and 1 for `yes’ or the equivalent. In this case, we are usually interested in modeling the probability of a ‘yes’. However, by default, SAS models the probability of a 0 (which would be a `no’).
Whenever you run a SAS program, you should look at the log file. In fact, I have set up keys on my SAS sessions so that when I hit F8, it submits and then goes to the log. One possible exception is when you are running the exact same program on different data, but, even here, I check the log. There might be something wrong with the data.
So, check the log! You’ll save yourself time and anguish and errors in the long run.
To set up your keys, press F9 and then edit the commands you see there.
Why we need survival analysis
When the dependent variable is continuous, we would ordinarily first think of linear regression. It’s a very good methods when you want to look at the relationship between a continuous dependent variable and one or more independent variables.
But, like nearly all statistical techniques, they make assumptions. And one of the assumptions that is so clear as to usually go unstated is that we know the value of the dependent variable; usually, this is not a problem. If we want to model, say, what people weigh, we can weigh them. But in one common type of analysis, we don’t always know the dependent variable – that’s when the dependent variable is time to an event. In that case, we need survival analysis.
I’ll bootstrap from here to eternity
‘Cause that’s my statistics fraternity
The idea is quite old
But it couldn’t be sold
Until we reached computer modernity
A young statistician name Myers
Says tenure is all he desires
But his dreams won’t be met,
He’ll be fired, I bet,
When they catch him adjusting his priors.
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