I find it a bit difficult to understand your problem. You might explain
what you are trying to measure or test rather than outline what you are
doing.
You say that you "pull the most recent 3 jobs within the standard deviation
threshold". If your data are normally distributed and there are
no anomalous data this will only take in 68% of the data on average. If you
adjust your limits to two standard deviations you will pull in about 95% of
the non-anomalous data i.e. you will miss 1/20 of the valid observations.
I suspect that you might be better using a Gamma or related distribution to
make inferences. Without more knowledge of the process I could not be
certain that this would make a difference.
Sample sizes of 3 or 10 are very small to draw conclusions.
You might seek advice from a statistician or quality control expert if that
is the purpose of the exercise. In any discipline it is often better to
seek the advice of an expert.
Best Regards
John
John C Frain
3 Aranleigh Park
Rathfarnham
Dublin 14
Ireland
mailto:frainj@tcd.ie
mailto:frainj@gmail.com
On 2 January 2015 at 20:15, <dts(a)dagey.com> wrote:
Hello All:
I apologize if this is not a proper use of the list, but you guys seem
like the best resource.
I have a question more related to statistical help than use of gretl. I
believe that I can work out the gretl commands but am unsure about the
statistical use/terminology.
We are a manufacturing facility and are using PHP and MySQL to process
operations through gretl (forecasting, linear regression, etc).
The data set for my current question is a series of job iterations and
associated times, such as the following:
Part # 1234
Job # Time (hours)
----------------------------------
1 2.0
2 2.5
3 1.8
4 1.9
5 6.7
6 2.2
7 5.0
8 2.3
9 1.9
10 2.2
What we need is to remove the anomalies from the data set, as we are doing
aggregation and the extraneous data points are throwing us off. They might
be due to rework or training, etc, and we want to calculate an accurate
average either without or minimizing these anomalies. For example, in the
dataset above, the times for job #5 & job #7 should most likely be
excluded, being over twice as much as the next highest time.
I have been using MySQL to calculate the standard deviation. We calculate
against the last 100 job numbers, then pull the most recent 3 jobs within
the standard deviation threshold and average them. However, this is only
useful **most** of the time. Sometimes the MySQL standard deviation
throws out good values that we need to keep, so I am looking for other
options.
My question is whether there is a better solution that calculating a
simple standard deviation, or how one might do so in gretl to filter the
dataset and remove anomalies? If another statistical function or operation
might be best, what would you suggest and how would we do so in gretl?
I understand this is a bit unorthodox, I am a developer with limited
statistical experience, so I appreciate any help you can provide.
Thanks,
--
Ryan Dagey
Chief Technology Officer:
www.NeotericSystems
www.NeotericHovercraft.com
www.WorldHovercraft.org
www.DiscoverHover.org
www.hovercrafttraining.com
Email: ryan(a)neotericsystems.com
Ph: 812-234-1120: 800-285-3761
Fax: 877-640-8507
Mail: 1649 Tippecanoe Street, Terre Haute, IN USA 47807-2394
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