Generating Normally Distributed Integers in R, Matlab, and Other Software

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SUMMARY

This discussion focuses on generating normally distributed integers in R and Matlab, specifically for modeling inter-arrival times. It highlights the impossibility of achieving a true normal distribution with integer-only outputs, as normal distributions can include negative values. The conversation emphasizes the importance of using appropriate distributions, such as exponential distributions for inter-arrival times, to avoid negative values and ensure valid modeling. Rounding decimal values can distort the normality of the data, depending on the standard deviation used.

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  • Understanding of normal distribution and its properties
  • Familiarity with R and Matlab programming languages
  • Knowledge of inter-arrival time modeling
  • Concept of rounding and its impact on statistical distributions
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Mark J.
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Hi.
Any ideas how to generate in any software R, Matlab etc normally distributed random numbers by one condition so they will be integers like 1, 2,3...and not decimal values.
I tried to round up the generated decimal values but in my guess normality is doubted after that.
Regards
 
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You will have to be clearer than this. A distribution which only takes on integers can never be normal.

What is it you really want??
 
Thanks,
I mean I need to generate some sample data like 9.00 AM 9.04 AM 9.07 AM but with condition that inter-arrival times are normally distributed.
That's why I need some numbers like 4 (9.04AM -9.00AM ) etc normally distributed.
Any help on this?
 
Arrivals are continuous. Your numbers will be real. You can round them if you like but if you do that then the approximation to normal depends on the parameters of the distribution.

I don't know what you're trying to do, but has it occurred to you that modeling arrivals with a normal distribution allows for the possibility of an arrival occurring before the previous arrival? You might want to consider that.
 
Can you please explain something more about?
Regards
 
I don't know which part you're referring to. For the first, just consider a normal density with s.d.=.01 vs. one with a very large s.d. If you draw from the first and round everything to the nearest integer then you basically end up with the mean for virtually all of your points. In the second case you could round to the nearest integer and still "approximate" normal, whatever that means in your context.

As to the second point, inter-arrival times are positive. T1 is the time from zero to the first arrival, T2 is the time from T1 of the second arrival, etc. So the times are usually described with a density that is zero for t<0 such as an exponential. If you use a normal distribution then you permit negative values and risk that, for example, T2<0, and your second arrival occurred prior to your first. You would take this into account in the context of what exactly you are trying to do.
 

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