# [CentralOH] OT: Statistician needed

Eric Floehr eric at intellovations.com
Mon May 16 18:56:12 EDT 2016

```Thanks Bim. I have no idea :-). I'm at the limits of my statistical
knowledge...

On Mon, May 16, 2016 at 4:11 PM, Bim Walker <bim at digitalbim.com> wrote:

>
> Can’t you just think of the set of errors as the raw data, and do an
> independent t-test comparing provider A to provider B?  (or an anova if you
> have more than two providers.)
>
>
> Bim
>
>
>
> On May 16, 2016, at 2:59 PM, Eric Floehr <eric at intellovations.com> wrote:
>
> Hey all,
>
> I'm in need of some help with statistics, and if anyone has any thoughts
> on this, or know someone who could do this, I would appreciate it greatly.
>
> I have a set of errors, normally distributed around 0 error (it's
> temperature forecast error). You can assume that the sample of forecasts is
> representative of the entire population (for example, taking 50 strategic
> locations around the U.S. to represent all U.S. locations).
>
> I then calculate the mean absolute error, and the RMSE. These have some
> positive value.
>
> What I would like to calculate on the MAE and RMSE is a confidence
> interval that the population error is within given the sample MAE or RMSE
> and it's related statistics (sample size, mean error, MAE, RMSE, standard
> deviation, etc.).
>
> For example, let's say that one provider's RMSE is 3.18 (A) and another's
> is 3.5 (B). I'd like to know with some confidence that there is (or isn't)
> a difference between providers (i.e. that provider A confidently has lower
> error than B).
>
> Currently, the way I'm doing it is using the normative inverse function in
> Excel:
>
> Lower bound: NORMINV(0.005,RMSE,STDDEV_RMSE/SQRT(NUMBER_OF_SAMPLES))
>
> Upper bound: NORMINV(0.995,RMSE,STDDEV_RMSE/SQRT(NUMBER_OF_SAMPLES))
>
> as in section 9.18 of:
>
> But I'm not at all convinced that I'm doing that right, or that it applies
> in this situation.
>
> Thanks so much!
> Eric
>
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