The data here come from a huge table of records of heart attack victims.
Getting tables into R is a bit complicated so use this
file which contains only the data on the DIED variable (coded
1=died). Save it on your hard drive in the directory where the R program
is located. If you name the file `DIED4R.txt`, you can use this R
command to input the data

> died = scan(file="DIED4R.txt") Read 12844 items

This puts the data into a variable called "died". Use `table`
on this variable to get counts if you do not already have them.

> table(died) died 0 1 11434 1410

1410 of the patients died. A single command gives confidence intervals
and tests any hypothetical *p*_{0} specified. Here we test
whether the results from this hospital match a hypothetical national
average of 10%. Ignore the `X-squared` value and use the *p*-value
for a hypothesis test. We need the number of 1's (from the `table`
command), the number of subjects (from `scan` or `length`),
and the hypothesized proportion.

> prop.test(1410,12844,p=0.1) 1-sample proportions test with continuity correction data: 1410 out of 12844, null probability 0.1 X-squared = 13.5385, df = 1, p-value = 0.0002337 alternative hypothesis: true p is not equal to 0.1 95 percent confidence interval: 0.1044507 0.1153421 sample estimates: p 0.1097789

We reject the hypothesis that the local proportion is the same as the
national proportion. However, the confidence interval indicates that it is
only slightly higher. Is that national average *exactly* 10.0000%?
Is there enough of a difference to matter?

© 2006 Robert W. Hayden