Documentation of 'jhplot.stat.Statistics' Java class.
Statistics
jhplot.stat

## Class Statistics

• `public class Statisticsextends Object`
A static class for statistical calculations.
• ### Constructor Summary

Constructors
Constructor and Description
`Statistics()`
• ### Method Summary

All Methods
Modifier and TypeMethod and Description
`static double[][]``correlation(double[][] v)`
Correlation
`static double[][]``correlation(double[][] v1, double[][] v2)`
Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)
`static double``correlation(double[] v1, double[] v2)`
Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)
`static double[][]``covariance(double[][] v)`
Covariance
`static double[][]``covariance(double[][] v1, double[][] v2)`
Covariance
`static double``covariance(double[] v1, double[] v2)`
Covariance
`void``doc()`
Show online documentation.
`static double``FProbability(double F, int df1, int df2)`
Computes probability of F-ratio.
`static double``gamma(double x)`
Returns the Gamma function of the argument.
`static double``incompleteBeta(double aa, double bb, double xx)`
Returns the Incomplete Beta Function evaluated from zero to xx.
`static double``incompleteBetaFraction1(double a, double b, double x)`
Continued fraction expansion #1 for incomplete beta integral.
`static double``incompleteBetaFraction2(double a, double b, double x)`
Continued fraction expansion #2 for incomplete beta integral.
`static double``lnGamma(double x)`
Returns natural logarithm of gamma function.
`static double``mean(double[] v)`
Get mean value
`static double[]``mean(double[][] v)`
Get mean
`static double``p1evl(double x, double[] coef, int N)`
Evaluates the given polynomial of degree N at x.
`static double``polevl(double x, double[] coef, int N)`
Evaluates the given polynomial of degree N at x.
`static double``powerSeries(double a, double b, double x)`
Power series for incomplete beta integral.
`static double``stddeviation(double[] v)`
Standard deviation
`static double[]``stddeviation(double[][] v)`
Standard deviation
`static double``stirlingFormula(double x)`
Returns the Gamma function computed by Stirling's formula.
`static double``variance(double[] v)`
Variance
`static double[]``variance(double[][] v)`
Variance
• ### Methods inherited from class java.lang.Object

`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Constructor Detail

• #### Statistics

`public Statistics()`
• ### Method Detail

• #### mean

`public static double mean(double[] v)`
Get mean value
Parameters:
`v` - vector
• #### mean

`public static double[] mean(double[][] v)`
Get mean
Parameters:
`v` - 2D array
Returns:
• #### stddeviation

`public static double stddeviation(double[] v)`
Standard deviation
Parameters:
`v` - vector
Returns:
• #### variance

`public static double variance(double[] v)`
Variance
Parameters:
`v` -
Returns:
vector
• #### stddeviation

`public static double[] stddeviation(double[][] v)`
Standard deviation
Parameters:
`v` -
Returns:
• #### variance

`public static double[] variance(double[][] v)`
Variance
Parameters:
`v` - vector
Returns:
• #### covariance

`public static double covariance(double[] v1,                                double[] v2)`
Covariance
Parameters:
`v1` - first vector
`v2` - second vector
Returns:
• #### covariance

`public static double[][] covariance(double[][] v1,                                    double[][] v2)`
Covariance
Parameters:
`v1` - first 2D array
`v2` - second 2D array
Returns:
• #### covariance

`public static double[][] covariance(double[][] v)`
Covariance
Parameters:
`v` -
Returns:
• #### correlation

`public static double correlation(double[] v1,                                 double[] v2)`
Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)
Parameters:
`v1` - first vector
`v2` - second vector
Returns:
• #### correlation

`public static double[][] correlation(double[][] v1,                                     double[][] v2)`
Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)
Parameters:
`v1` - first vector
`v2` - second vector
Returns:
• #### correlation

`public static double[][] correlation(double[][] v)`
Correlation
Parameters:
`v` -
Returns:
• #### FProbability

`public static double FProbability(double F,                                  int df1,                                  int df2)`
Computes probability of F-ratio.
Parameters:
`F` - the F-ratio
`df1` - the first number of degrees of freedom
`df2` - the second number of degrees of freedom
Returns:
the probability of the F-ratio.
• #### incompleteBeta

`public static double incompleteBeta(double aa,                                    double bb,                                    double xx)`
Returns the Incomplete Beta Function evaluated from zero to xx.
Parameters:
`aa` - the alpha parameter of the beta distribution.
`bb` - the beta parameter of the beta distribution.
`xx` - the integration end point.
• #### powerSeries

`public static double powerSeries(double a,                                 double b,                                 double x)`
Power series for incomplete beta integral. Use when b*x is small and x not too close to 1.
• #### lnGamma

`public static double lnGamma(double x)`
Returns natural logarithm of gamma function.
Parameters:
`x` - the value
Returns:
natural logarithm of gamma function
• #### p1evl

`public static double p1evl(double x,                           double[] coef,                           int N)`
Evaluates the given polynomial of degree N at x. Evaluates polynomial when coefficient of N is 1.0. Otherwise same as polevl().
`                     2          N y  =  C  + C x + C x  +...+ C x        0    1     2          N  Coefficients are stored in reverse order:  coef = C  , ..., coef[N] = C  .            N                   0 `
The function p1evl() assumes that coef[N] = 1.0 and is omitted from the array. Its calling arguments are otherwise the same as polevl().

In the interest of speed, there are no checks for out of bounds arithmetic.

Parameters:
`x` - argument to the polynomial.
`coef` - the coefficients of the polynomial.
`N` - the degree of the polynomial.
• #### gamma

`public static double gamma(double x)`
Returns the Gamma function of the argument.
• #### stirlingFormula

`public static double stirlingFormula(double x)`
Returns the Gamma function computed by Stirling's formula. The polynomial STIR is valid for 33 <= x <= 172.
• #### polevl

`public static double polevl(double x,                            double[] coef,                            int N)`
Evaluates the given polynomial of degree N at x.
`                     2          N y  =  C  + C x + C x  +...+ C x        0    1     2          N  Coefficients are stored in reverse order:  coef = C  , ..., coef[N] = C  .            N                   0 `
In the interest of speed, there are no checks for out of bounds arithmetic.
Parameters:
`x` - argument to the polynomial.
`coef` - the coefficients of the polynomial.
`N` - the degree of the polynomial.
• #### incompleteBetaFraction1

`public static double incompleteBetaFraction1(double a,                                             double b,                                             double x)`
Continued fraction expansion #1 for incomplete beta integral.
• #### incompleteBetaFraction2

`public static double incompleteBetaFraction2(double a,                                             double b,                                             double x)`
Continued fraction expansion #2 for incomplete beta integral.
• #### doc

`public void doc()`
Show online documentation.

DMelt 2.7 © DataMelt by jWork.ORG