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Java: How to determine programmatically that a dataset doesn't follow a normal distribution?

By : Mifan Ardana
Date : October 17 2020, 11:12 AM
it should still fix some issue There are two questions here: how to determine if a distribution is normal and how to do so in Java. As the first link will show you, there are varying degrees of how certain you want to be that you are looking at normal data from the formal to the informal. The second link shows that there aren't standard Java packages for statistical analysis but many other ways to implement them.
code :

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how to generate random numbers that follow skew normal distribution in matlab

By : Steven
Date : March 29 2020, 07:55 AM
I wish this help you Can't vouch for their performance/adequacy, but http://azzalini.stat.unipd.it/SN/ says the following, and has a link to a .zip file of MATLAB functions:
code :
Function RandSkew(fAlpha As Single, _
                  Optional fLocation As Single = 0, _
                  Optional fScale As Single = 1, _
                  Optional bVolatile As Boolean = False) As Single

    ' shg 2008-0919
    ' http://azzalini.stat.unipd.it/SN/faq.html

    ' Returns a random variable with skewed distribution
    '       fAlpha      = skew
    '       fLocation   = location
    '       fScale > 0  = scale

    Dim sigma   As Single
    Dim afRN()  As Single
    Dim u0      As Single
    Dim v       As Single
    Dim u1      As Single

    If bVolatile Then Application.Volatile
    Randomize (Timer)

    sigma = fAlpha / Sqr(1 + fAlpha ^ 2)

    afRN = RandNorm()
    u0 = afRN(1)
    v = afRN(2)
    u1 = sigma * u0 + Sqr(1 - sigma ^ 2) * v

    RandSkew = IIf(u0 >= 0, u1, -u1) * fScale + fLocation
End Function

R Creating Normal Distribution Plot using dataset

By : mniedero
Date : March 29 2020, 07:55 AM
may help you . Im new to R.
code :
  xlab("x value")+ylab("Density")+
  ggtitle("Sample Means of Exponential Distribution")

Normal distribution function:determine probability of a given point in Java

By : user2987955
Date : March 29 2020, 07:55 AM
like below fixes the issue First of all, the question cannot be answered as is because in a continous distribution like the normal distribution, the probability of an specific point is always zero. You need to ask yourself what it is exactly you want to know in terms of an interval.
For example, cern.jet.stat.Probability.normal(double) will answer the question "What is the probability of the value being less than my value?" (Less than or equal is equivalent in this context.)
code :
xn = (x - mean) / standard deviation
2 * CPD( -abs(xn) )

Does a dataset need to be a normal distribution for every parameter?

By : Bima
Date : March 29 2020, 07:55 AM
I wish this help you As cel said, every model has its own assumptions and limitations. While there might be a model that can only learn on completely normally distributed data - there are plenty of models which don't, such as SVMs or Random Forests.
In practice if you know that your data does not conform to the assumptions of your model you could consider using a different model or to manipulate your data to fit your assumption. The latter option is something that you should consider carefully to make sure your manipulation does not render your model useless when used in real-life scenarios.

example algorithm for generating random value in dataset with normal distribution?

By : user3933725
Date : March 29 2020, 07:55 AM
I wish this helpful for you Box-Muller transform in a nutshell:
First, get two independent, uniform random numbers from the interval (0, 1], call them U and V.
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