Quote:
Originally Posted by Ether
Getting back to this after a long hiatus.
If you are asking for individual standard error associated with each OPR value, no one ever posts them because the official FRC match data doesn't contain enough information to make a meaningful computation of those individual values.
In a situation, unlike FRC OPR, where you know the variance of each observed value (either by repeated observations using the same values for the predictor variables, or if you are measuring something with an instrument of known accuracy) you can put those variances into the design matrix for each observation and compute a meaningful standard error for each of the model parameters.
Or if, unlike FRC OPR, you have good reason to believe the observations are homoscedastic, you can compute the variance of the residuals and use that to back-calculate standard errors for the model parameters. If you do this for FRC data the result will be standard errors which are very nearly the same for each OPR value... which is clearly not the expected result.
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The standard errors for the OPR values can be computed, but they are in fact quite large relative to the parameter values. Which is actually my point--the statistical precision of the OPR values are really quite poor because there are so few observations, which are in fact not independent. Rather than ignoring the SEs because they show how poor the OPR estimators are performing, the SEs should be reported to show how poorly the estimators perform for everyone's consideration.