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java.lang.Objectpapaya.Normality
public class Normality
Contains various utilities for checking if the dataset comes from a normal distribution.
Nested Class Summary | |
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static class |
Normality.Dago
Methods for computing the skewnewss, kurtosis, and D'Agostino-Peasrson K^2 "omnibus" test-statistics (that combine the former two), and accompanying significance (or p-values) for testing the underlying population normality. |
Field Summary |
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Fields inherited from interface papaya.PapayaConstants |
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BASELINE, big, biginv, BOTTOM, CENTER, CORNER, FONTNAME, GRAY, INDEX_NOT_FOUND, INDICES_NOT_FOUND, LEFT, LOGPI, MACHEP, MAXGAM, MAXLOG, MINLOG, RIGHT, SQRTH, SQTPI, STROKEWEIGHT, TEXTSIZE, TOP |
Method Summary | |
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static float[] |
normalProbability(float[] data)
Return the Normal order statistic medians ( N ) necessary to produce a Q-Q plot
for a normal distribution (or normal probability plot). |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Method Detail |
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public static float[] normalProbability(float[] data)
N
) necessary to produce a Q-Q plot
for a normal distribution (or normal probability plot).
That is, given a data array, this method computes the Normal order statistic medians
and the ordered response values or z
-scores of the input.
A plot of the N
vs z
should form an approximate straight line if the
data is normally distributed;
departures from this straight line indicate departures from normality with the shape of the line
providing clues as to the distribution of the data.
The normal order statistic medians are computed as
N[i] = Probability.norminv(U[i])
where U[i]
are the uniform order statistic medians, given by
Reference: NIST/SEMATECH e-Handbook of Statistical Methods, EDA Section 1.3.3.21: Normal Probability Plots
data
- the data array
Descriptive.zScore(float[],float,float)
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