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08-10-2015 12:15 PM

Dear community members,

I performed an analysis of customers inter-arrival times for my case and I have a list of 196 observations (each observation is the inter-arrival time between 2 customers). I used the data fit tool but no distribution fits the data. Then I’m trying to use an empirical continuous distribution.

The problem is that I should define the number of classes, the upper bound and the lower bound of each class.

I thought I could use as classes the same that the data fit creates (picture below)

It actually tells me that I could use 14 classes, but what about the lower and upper bound of each class?

Do you have any suggestion on that?

Solved! Go to Solution.

4 REPLIES 4

Re: Continuous empirical distribution

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08-11-2015 04:43 AM

Hi Alessandro,

I'd recommend to choose for the boundaries the mid value of the intervals:

0, (210+350)/2, ~56

(210+350)/2, (350+490)/2, ~31

.

.

.

Regards,

Ralf

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08-11-2015 05:28 AM

Hi Ralf,

thanks for your reply.

I just saw that in the tab "evaluation" there is "frequency table". This is the view

I guess it is the way the Datafit divided the input data, along with the the frequencies. Is this good for the empirical distribution? I mean, can I just insert these data instead of doing the calculation you suggested before?

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08-11-2015 05:56 AM

Right, this is the better choice, since you can just copy these values.

Regards,

Ralf

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Re: Continuous empirical distribution

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3 weeks ago

Mr. @RalfTobel

I'm facing a similar situation, I have different samples of failures times, I used the Data Fit Tool to find a data distribution, but many of my samples don't feet to any, so I need to use several empirical distributions. Here is the question.

¿Has a simulation model reliability when it uses several empirical distributions?

I think the problem is in my data samples because even analyzing and doing an exercise where I eliminate the outliers, the sample doesn't feet to any distribution.

Regards

Antonio

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