[time-nuts] Long Wave Radio-Frequency standard testing

[Dave Daniel]

Or one can replace those values with zero. That eliminates them; averaging then proceeds without those values altering the most probable correct average.

DaveD

> On Jan 19, 2021, at 08:49, Bob kb8tq <kb8tq at n1k.org> wrote:
> 
> Hi
> 
> The normal approach to filling a gap is to put in a point that is the average
> of the two adjacent points. The assumption is that this is a “safe” value that
> will not blow up the result. That’s probably ok if it is done rarely. The risk is
> that you are running a filter process (averaging is a low pass filter). 
> 
> If you pull out a *lot* of outliers and replace them, you are doing a lot of filtering.
> Since you are measuring noise, filtering is very likely to improve the result. 
> The question becomes - how representative is the result after a lot of this or 
> that has been done? 
> 
> Obviously the answer to all this depends on what you are trying to do. If you
> are running a control loop and the output improves, that’s fine. If you are 
> trying to provide an accurate measure of noise …. maybe not so much :) 
> 
> Bob
> 
>> On Jan 19, 2021, at 2:15 AM, Gilles Clement <clemgill at gmail.com> wrote:
>> 
>> Hi, 
>> Yes outliers removal creates gap in Stable32.
>> The « fill »  function can fills gaps with interpolated values. 
>> It does not change much the graphs, except in the low Tau area (see attached). 
>> Do you know a discussion of impact of outliers removal ? 
>> Gilles. 
>> 
>> 
>> 
>>> Le 18 janv. 2021 à 22:06, Bob kb8tq <kb8tq at n1k.org> a écrit :
>>> 
>>> Hi
>>> 
>>> As you throw away samples that are far off the mean, you reduce the sample
>>> rate ( or at least create gaps in the record). Dealing with that could be difficult.
>>> 
>>> Bob
>>> 
>>>>> On Jan 18, 2021, at 1:33 PM, Gilles Clement <clemgill at gmail.com> wrote:
>>>>> 
>>>>> Hi
>>>>> 
>>>>> Very cool !!!
>>>>> 
>>>>> The red trace is obviously the one to focus on. Some sort of digital loop that
>>>>> only operates under the “known good” conditions would seem to make sense. 
>>>>> 
>>>>> Thanks for sharing 
>>>>> 
>>>>> Bob
>>>> 
>>>> Hi,
>>>> I tried something with the idea to consider night records fluctuations as « outliers » as compared to day records. 
>>>> Indeed the 3 days record mean value is flat and the histogram quite gaussian. 
>>>> So I processed the 3 days record (green trace) with Stable32’s « Check Function »,
>>>> while removing outliers with decreasing values of the Sigma Factor. The graph below shows the outcome. 
>>>> The graph with Sigma=0.8 (blue trace) connects rather well with the 1Day record (red trace). 
>>>> Would this be a workable approach ? 
>>>> Best, 
>>>> Gilles. 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> _______________________________________________
>>>> time-nuts mailing list -- time-nuts at lists.febo.com
>>>> To unsubscribe, go to http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com
>>>> and follow the instructions there.
>>> 
>>> 
>>> _______________________________________________
>>> time-nuts mailing list -- time-nuts at lists.febo.com
>>> To unsubscribe, go to http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com
>>> and follow the instructions there.
>> 
>> _______________________________________________
>> time-nuts mailing list -- time-nuts at lists.febo.com
>> To unsubscribe, go to http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com
>> and follow the instructions there.
> 
> 
> _______________________________________________
> time-nuts mailing list -- time-nuts at lists.febo.com
> To unsubscribe, go to http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com
> and follow the instructions there.