Quote:
Originally Posted by Ed Law
However the engineer in me tells me that it is a waste of time. Based on the noise factors I listed above and that the robot performance may change over time, this becomes just a mathematical exercise and does not have much contribution to the prediction of outcome of the next match.
However I do support the publication of the R^2 coefficient of determination. It will give an overall number as to how well the actual outcome fits the statistical model.
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The statistician in me led to asking this question. One aspect is that I believe publishing standard errors for parameter estimates provides greater transparency. Plus it is very educational. I suspect that most students looking at OPRs don't understand that they are actually statistical estimates with large error bands around the parameter estimates. Providing that education is directly in line with our STEM mission. Too many engineers don't understand the implications and importance of statistical properties in their own work (I see it constantly in my professional life.)
And regardless I think see the SEs lets us see if a team has a more variable performance than another. That's another piece of information that we can then use to explore it further. For example is the variability arising because parts keep breaking or is there an underlying improvement trend through the competition--either one would increase the SE compared to a steady performance rate. There's other tools for digging into that data, but we may not look unless we have that SE measure first.