07-31-2017 02:42 AM
I have a simplified dataset: runner name, run trial (there are 10 trials), observed time for each trial, expected time for each trial.
I sum up my observed and expected times using proc means and output a dataset.I then use the data statement to calculated a ratio for observed / expected for each runner. how do i calculate a confidence interval to see if there is a difference in o/e between my runners?
07-31-2017 03:13 AM
I'm not sure if this is what you are efter, but you can request confidence limits for the mean by specifying the CLM option in the PROC MEANS statement.
An example is in the PROC MEANS documentation here
08-01-2017 01:28 PM
You should post some sample data, which will make it easier for people to help you.
You might consider the difference in the runner's time, rather than the ratio. If you have several runners and you want to compare their mean times and determine if the mean times are significantly different, you can use PROC GLM to run an ANOVA and a multiple comparison of means. There are several examples in the PROC GLM doc, including one that treats repeated observations.
09-14-2017 02:55 PM
My sample data look like this:
|runner_name||run trial||observed time (minutes)||expected time (minutes)|
I sum up my observed and expected times using proc means and output a dataset.I then use the data statement to calculated a ratio for observed / expected for each runner.
I want to see if my overall observed/expected ratio is significantly different between runners. I'd like to generate 95% confidence intervals. Can I do this in SAS? This is not a binomial proportion so i can't use proc freq to calculate CIs.
09-14-2017 03:35 PM
Given the irregular values in your table of expected run times, I presume your runners are not all running the same set of distances, ... or are otherwise not subjected to the same set of running conditions. So I can see why you are considering O/E instead of O-E. But maybe if O is a predictor var, you can still use O-E as the dependent var.