Simulation, distribution, statistics.

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To determine the best-fitting distribution for a dataset with a mean of 3.206, variance of 2.958195, and skewness of 0.945, the discussion emphasizes the use of estimators to find parameters like alpha and beta for various distributions. The Kolmogorov-Smirnov (KS) test is suggested as a method to assess the goodness of fit for potential distributions such as Geometric, Poisson, Weibull, Gamma, and PT5. The importance of plotting the data and calculating the probability density function is highlighted as essential steps in the analysis. Participants discuss different types of estimators, including Method of Moments and Maximum Likelihood Estimators, which are relevant for fitting distributions based on the data characteristics. Overall, the conversation centers on statistical methods for identifying the appropriate distribution for the given dataset.
skaterboy1
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I´m wondering how to find out something about distribution functions after I´ve calculated the scewness. I have 15 numbers, mean is 3.206, highest is 7.028 and lowest is 0.9209. Variance is 2.958195. I calculate the skewness and got 0.945.

I´m wondering which of the following distributions is the best match for the numbers I´ve already calculated. I tried to plot the scors I got but it didn´t seem to make any sense.

These are the possibilities:
DU(i,j)).
Geom(p)).
Poisson(lambda).
Weibull(alpha,beta)).
Gamma(alpha,beta).
PT5(alpha,beta).

I was looking for a way to find alpha, beta, gamma etc
I was thinking if it was something about the Kolmogorov test?
 
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Hey skaterboy1 and welcome to the forums.

For this kind of problem, we use what is called an estimator.

An estimator is used to get an idea of what a parameter of a distribution might be given a set of data. The estimators that statisticians and scientists used have the properties that they are consistent, unbiased and reliable.

We have different kinds of estimators like Method of Moments and Maximum Likelihood Estimators. These templates can be applied for general distributions if the distribution has certain properties (for example MLE can't be applied to Uniform distributions because it is a constant function which screws up the estimator).

What is your mathematical and statistical background like?
 
Thank you for the reply. Average I guess. Studying engineering.
I red more about this and I have to find probability density function and also plot the numbers i have. I have to do the KS test and find out what type of distribution this is.
I don´t know how to start.
 
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