Catalano.Statistics

Class DescriptiveStatistics

java.lang.Object

Catalano.Statistics.DescriptiveStatistics

public final class DescriptiveStatistics
extends java.lang.Object

Descriptive statistics are used to describe the basic features of the data in a study.
They provide simple summaries about the sample and the measures.
@ref http://www.socialresearchmethods.net/kb/statdesc.php

Method Summary

Modifier and Type	Method and Description
static double	Kurtosis(double[] values) Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution.
static double	Kurtosis(double[] values, double mean, double stdDeviation) Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution.
static double	Maximum(double[] values) The maximum is the maximum among sets of values.
static double	Mean(double[] values) The Mean or average is probably the most commonly used method of describing central tendency.
static float	Mean(float[] values) The Mean or average is probably the most commonly used method of describing central tendency.
static double	Mean(int[] values) The Mean or average is probably the most commonly used method of describing central tendency.
static double	Median(double[] values) The Median is the score found at the exact middle of the set of values.
static double	Minimum(double[] values) The minimum is the minimum among sets of values.
static double	Mode(double[] values) The mode is the most frequently occurring value in the set of scores.
static double	Range(double[] values) The range is simply the highest value minus the lowest value.
static double	Skewness(double[] values) Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
static double	Skewness(double[] values, double mean, double stdDeviation) Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
static double	StandartDeviation(double[] values) The Standard Deviation is a more accurate and detailed estimate of dispersion.
static double	StandartDeviation(double[] values, double mean) The Standard Deviation is a more accurate and detailed estimate of dispersion.
static double	Variance(double[] values) The variance is a measure of how far a set of numbers is spread out.
static double	Variance(double[] values, double mean) The variance is a measure of how far a set of numbers is spread out.
static float	Variance(float[] values) The variance is a measure of how far a set of numbers is spread out.
static float	Variance(float[] values, float mean) The variance is a measure of how far a set of numbers is spread out.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

Mean

public static double Mean(double[] values)

The Mean or average is probably the most commonly used method of describing central tendency.
To compute the mean is add up all the values and divide by the number of values.

Example:
1, 2, 3 ,4 ,5
Mean: 3

Parameters:

values -

Returns:

Mean.

Mean

public static float Mean(float[] values)

The Mean or average is probably the most commonly used method of describing central tendency.
To compute the mean is add up all the values and divide by the number of values.

Example:
1, 2, 3 ,4 ,5
Mean: 3

Parameters:

values -

Returns:

Mean.

Mean

public static double Mean(int[] values)

The Mean or average is probably the most commonly used method of describing central tendency.
To compute the mean is add up all the values and divide by the number of values.

Example:
1, 2, 3 ,4 ,5
Mean: 3

Parameters:

values -

Returns:

Mean.

Median

public static double Median(double[] values)

The Median is the score found at the exact middle of the set of values.

Example:
5, 3, 8 ,4 ,0
0, 3, 4, 5, 8
Median: 4

Parameters:

values - Set of values.

Returns:

Median.

Mode

public static double Mode(double[] values)

The mode is the most frequently occurring value in the set of scores.

Example:
5, 5, 8 ,4 ,0
0, 4, 5, 5, 8
Mode: 5

Parameters:

values - Set of values.

Returns:

Mode.

Minimum

public static double Minimum(double[] values)

The minimum is the minimum among sets of values.

Parameters:

values - Set of values.

Returns:

Minimum.

Maximum

public static double Maximum(double[] values)

The maximum is the maximum among sets of values.

Parameters:

values - Set of values.

Returns:

Maximum.

Variance

public static double Variance(double[] values,
                              double mean)

The variance is a measure of how far a set of numbers is spread out.

Parameters:

values - Set of values.

mean - Mean.

Returns:

Variance.

Variance

public static float Variance(float[] values,
                             float mean)

The variance is a measure of how far a set of numbers is spread out.

Parameters:

values - Set of values.

mean - Mean.

Returns:

Variance.

Variance

public static double Variance(double[] values)

The variance is a measure of how far a set of numbers is spread out.

Parameters:

values - Set of values.

Returns:

Variance.

Variance

public static float Variance(float[] values)

The variance is a measure of how far a set of numbers is spread out.

Parameters:

values - Set of values.

Returns:

Variance.

Range

public static double Range(double[] values)

The range is simply the highest value minus the lowest value.

Example:
1, 8, 2 ,5 ,7
Range: 7

Parameters:

values - Sets of values.

Returns:

Range.

StandartDeviation

public static double StandartDeviation(double[] values)

The Standard Deviation is a more accurate and detailed estimate of dispersion.
The Standard Deviation shows the relation that set of scores has to the mean of the sample.

Parameters:

values - Set of values.

Returns:

Standart deviation.

StandartDeviation

public static double StandartDeviation(double[] values,
                                       double mean)

The Standard Deviation is a more accurate and detailed estimate of dispersion.
The Standard Deviation shows the relation that set of scores has to the mean of the sample.

Parameters:

values - Set of values.

mean - Meaan.

Returns:

Standart deviation.

Kurtosis

public static double Kurtosis(double[] values,
                              double mean,
                              double stdDeviation)

Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution.

Parameters:

values - Set of values.

mean - Mean.

stdDeviation - Standart deviation.

Returns:

Kurtosis.

Kurtosis

public static double Kurtosis(double[] values)

Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution.

Parameters:

values - Set of values.

Returns:

Kurtosis.

Skewness

public static double Skewness(double[] values,
                              double mean,
                              double stdDeviation)

Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.

Parameters:

values - Set of values.

mean - Mean.

stdDeviation - Standart deviation.

Returns:

Skewness.

Skewness

public static double Skewness(double[] values)

Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.

Parameters:

values - Set of values.

Returns:

Skewness.