java.lang.Object
- jsl.utilities.statistic.AbstractCollector
- - jsl.utilities.statistic.AbstractStatistic
  - - jsl.utilities.statistic.Statistic

All Implemented Interfaces:

java.lang.Comparable<AbstractStatistic>, GetNameIfc, IdentityIfc, ArraySaverIfc, CollectorIfc, GetCSVStatisticIfc, StatisticAccessorIfc
```
public class Statistic
extends AbstractStatistic
```
The Statistic class allows the collection of summary statistics on data via the collect() methods. The primary statistical summary is for the statistical moments.

Field Summary

Fields
Modifier and Type	Field	Description
`protected double`	`firstx`	Holds the first observed data point, needed for von-neuman statistic
`protected double`	`max`	Holds the maximum of the observed data
`protected double`	`min`	Holds the minimum of the observed data.
`protected double[]`	`moments`	Holds the first 4 statistical central moments
`protected double`	`myJsum`	Holds sum = sum + j*x
`protected double`	`myValue`	Holds the last value observed
`protected double`	`num`	Holds the number of observations observed
`protected double`	`sumxx`	Holds the sum the lag-1 data, i.e.

Fields inherited from class jsl.utilities.statistic.AbstractStatistic
DEFAULT_CONFIDENCE_LEVEL, myConfidenceLevel, myNumMissing

Fields inherited from interface jsl.utilities.statistic.ArraySaverIfc
DEFAULT_DATA_ARRAY_SIZE

Constructor Summary

Constructors
Constructor	Description
`Statistic()`	Creates a Statistic with name "null"
`Statistic(double[] values)`	Creates a Statistic \based on the provided array
`Statistic(java.lang.String name)`	Creates a Statistic with the given name
`Statistic(java.lang.String name, double[] values)`	Creates a Statistic with the given name based on the provided array

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`java.lang.String`	`asString()`
`boolean`	`checkMean(double mean)`	Checks if the supplied value falls within getAverage() +/- getHalfWidth()
`void`	`collect(double x)`	Collect on the supplied value
`static Statistic`	`collectStatistics(double[] x)`	Returns a statistic that summarizes the passed in array of values
`static int`	`countGreaterEqualTo(double[] data, double x)`
`static int`	`countGreaterThan(double[] data, double x)`
`static int`	`countLessEqualTo(double[] data, double x)`
`static int`	`countLessThan(double[] data, double x)`
`long`	`estimateSampleSize(double desiredHW)`	Estimates the number of observations needed in order to obtain a getConfidenceLevel() confidence interval with plus/minus the provided half-width
`static long`	`estimateSampleSize(double desiredHW, double stdDev, double level)`	Estimate the sample size based on a normal approximation
`double`	`get2ndCentralMoment()`	Returns the 2nd statistical central moment
`double`	`get2ndRawMoment()`	Returns the 2nd statistical raw moment (about zero)
`double`	`get3rdCentralMoment()`	Returns the 3rd statistical central moment
`double`	`get3rdRawMoment()`	Returns the 3rd statistical raw moment (about zero)
`double`	`get4thCentralMoment()`	Returns the 4th statistical central moment
`double`	`get4thRawMoment()`	Returns the 4th statistical raw moment (about zero)
`double`	`getAverage()`	Gets the unweighted average of the observations.
`double[]`	`getCentralMoments()`	The 0th moment is the count, the 1st central moment zero, the 2nd, 3rd, and 4th central moments
`double`	`getCount()`	Gets the count of the number of the observations.
`double`	`getDeviationSumOfSquares()`	Gets the sum of squares of the deviations from the average This is the numerator in the classic sample variance formula
`double`	`getHalfWidth(double level)`	Returns the half-width for a confidence interval on the mean with confidence level based on StudentT distribution
`static int`	`getIndexOfMax(double[] x)`	Returns the index associated with the maximum element in the array For ties, this returns the first found
`static int`	`getIndexOfMin(double[] x)`	Returns the index associated with the minimum element in the array For ties, this returns the first found
`double`	`getKurtosis()`	Gets the kurtosis of the data
`double`	`getLag1Correlation()`	Gets the lag-1 generate correlation of the unweighted observations.
`double`	`getLag1Covariance()`	Gets the lag-1 generate covariance of the unweighted observations.
`double`	`getLastValue()`	Gets the last observed data point
`int`	`getLeadingDigitRule(double a)`	Computes the right most meaningful digit according to (int)Math.floor(Math.log10(a*getStandardError())) See doi 10.1287.opre.1080.0529 by Song and Schmeiser
`double`	`getMax()`	Gets the maximum of the observations.
`static double`	`getMax(double[] x)`
`static double`	`getMedian(double[] data)`	Returns the median of the data.
`double`	`getMin()`	Gets the minimum of the observations.
`static double`	`getMin(double[] x)`
`double`	`getObsWeightedSum()`	Returns the observation weighted sum of the data i.e.
`static double[]`	`getOrderStatistics(double[] data)`
`double`	`getSkewness()`	Gets the skewness of the data
`double`	`getStandardError()`	Gets the standard error of the observations.
`double`	`getSum()`	Gets the sum of the observations.
`java.lang.String`	`getSummaryStatistics()`	Returns the summary statistics values Name Count Average Std.
`java.lang.String`	`getSummaryStatisticsHeader()`	Returns the header for the summary statistics Name Count Average Std.
`double`	`getVariance()`	Gets the sample variance of the observations.
`double`	`getVonNeumannLag1TestStatistic()`	Gets the Von Neumann Lag 1 test statistic for checking the hypothesis that the data are uncorrelated Note: See Handbook of Simulation, Jerry Banks editor, McGraw-Hill, pg 253.
`double`	`getVonNeumannLag1TestStatisticPValue()`	Returns the asymptotic p-value for the Von Nueumann Lag-1 Test Statistic:
`Statistic`	`newInstance()`	Creates a instance of Statistic that is a copy of the supplied Statistic All internal state is the same (including whether the collection is on or off) and the collection rule.
`static Statistic`	`newInstance(Statistic stat)`	Creates a instance of Statistic that is a copy of the supplied Statistic All internal state is the same (including whether the collection is on or off) and the collection rule.
`void`	`reset()`	Resets the collector as if no observations had been collected.
`java.lang.String`	`toString()`	Returns a String representation of the Statistic

Methods inherited from class jsl.utilities.statistic.AbstractStatistic
compareTo, getConfidenceLevel, getNumberMissing, setConfidenceLevel

Methods inherited from class jsl.utilities.statistic.AbstractCollector
clearSavedData, getId, getName, getSavedData, getSaveOption, save, save, setArraySizeIncrement, setName, setSaveOption

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface jsl.utilities.statistic.CollectorIfc
collect, collect, collect

Methods inherited from interface jsl.utilities.statistic.StatisticAccessorIfc
getConfidenceInterval, getConfidenceInterval, getCSVHeader, getCSVStatistic, getCSVStatisticHeader, getCSVValues, getHalfWidth, getName, getRelativeError, getRelativeWidth, getRelativeWidth, getStandardDeviation, getStatistics, getStatisticsAsMap

- Field Detail
  - min
```
protected double min
```
    Holds the minimum of the observed data.
  - max
```
protected double max
```
    Holds the maximum of the observed data
  - num
```
protected double num
```
    Holds the number of observations observed
  - myValue
```
protected double myValue
```
    Holds the last value observed
  - sumxx
```
protected double sumxx
```
    Holds the sum the lag-1 data, i.e. from the second data point on variable for collecting lag1 covariance
  - firstx
```
protected double firstx
```
    Holds the first observed data point, needed for von-neuman statistic
  - moments
```
protected double[] moments
```
    Holds the first 4 statistical central moments
  - myJsum
```
protected double myJsum
```
    Holds sum = sum + j*x
- Constructor Detail
  - Statistic
```
public Statistic()
```
    Creates a Statistic with name "null"
  - Statistic
```
public Statistic(java.lang.String name)
```
    Creates a Statistic with the given name
    
    Parameters:
    
    name - A String representing the name of the statistic
  - Statistic
```
public Statistic(double[] values)
```
    Creates a Statistic \based on the provided array
    
    Parameters:
    
    values - an array of values to collect statistics on
  - Statistic
```
public Statistic(java.lang.String name,
                 double[] values)
```
    Creates a Statistic with the given name based on the provided array
    
    Parameters:
    
    name - A String representing the name of the statistic
    
    values - an array of values to collect statistics on
- Method Detail
  - collectStatistics
```
public static Statistic collectStatistics(double[] x)
```
    Returns a statistic that summarizes the passed in array of values
    
    Parameters:
    
    x - the values to compute statistics for
    
    Returns:
    
    a Statistic summarizing the data
  - newInstance
```
public static Statistic newInstance(Statistic stat)
```
    Creates a instance of Statistic that is a copy of the supplied Statistic All internal state is the same (including whether the collection is on or off) and the collection rule. The only exception is for the id of the returned Statistic If this statistic has saved data, the new instance will also have that data.
    
    Parameters:
    
    stat - the stat to copy
    
    Returns:
    
    a copy of the supplied Statistic
  - newInstance
```
public final Statistic newInstance()
```
    Creates a instance of Statistic that is a copy of the supplied Statistic All internal state is the same (including whether the collection is on or off) and the collection rule. The only exception is for the id of the returned Statistic. If this statistic has saved data, the new instance will also have that data.
    
    Returns:
    
    a copy of the supplied Statistic
  - getIndexOfMin
```
public static int getIndexOfMin(double[] x)
```
    Returns the index associated with the minimum element in the array For ties, this returns the first found
    
    Parameters:
    
    x - the array of data
    
    Returns:
    
    the index associated with the minimum element
  - getMin
```
public static double getMin(double[] x)
```
    Parameters:
    
    x - the array of data
    
    Returns:
    
    the minimum value in the array
  - getIndexOfMax
```
public static int getIndexOfMax(double[] x)
```
    Returns the index associated with the maximum element in the array For ties, this returns the first found
    
    Parameters:
    
    x - the array of data
    
    Returns:
    
    the index associated with the maximum element
  - getMax
```
public static double getMax(double[] x)
```
    Parameters:
    
    x - the array of data
    
    Returns:
    
    the maximum value in the array
  - getMedian
```
public static double getMedian(double[] data)
```
    Returns the median of the data. The array is sorted
    
    Parameters:
    
    data - the array of data, must not be null
    
    Returns:
    
    the median of the data
  - countLessEqualTo
```
public static int countLessEqualTo(double[] data,
                                   double x)
```
    Parameters:
    
    data - the data to count
    
    x - the ordinate to check
    
    Returns:
    
    the number of data points less than or equal to x
  - countLessThan
```
public static int countLessThan(double[] data,
                                double x)
```
    Parameters:
    
    data - the data to count
    
    x - the ordinate to check
    
    Returns:
    
    the number of data points less than x
  - countGreaterEqualTo
```
public static int countGreaterEqualTo(double[] data,
                                      double x)
```
    Parameters:
    
    data - the data to count
    
    x - the ordinate to check
    
    Returns:
    
    the number of data points greater than or equal to x
  - countGreaterThan
```
public static int countGreaterThan(double[] data,
                                   double x)
```
    Parameters:
    
    data - the data to count
    
    x - the ordinate to check
    
    Returns:
    
    the number of data points greater than x
  - getOrderStatistics
```
public static double[] getOrderStatistics(double[] data)
```
    Parameters:
    
    data - the data to sort
    
    Returns:
    
    a copy of the sorted array in ascending order representing the order statistics
  - getCount
```
public final double getCount()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the count of the number of the observations.
    
    Returns:
    
    A double representing the count
  - getSum
```
public final double getSum()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the sum of the observations.
    
    Returns:
    
    A double representing the unweighted sum
  - getAverage
```
public final double getAverage()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the unweighted average of the observations.
    
    Returns:
    
    A double representing the average or Double.NaN if no observations.
  - get2ndCentralMoment
```
public final double get2ndCentralMoment()
```
    Returns the 2nd statistical central moment
    
    Returns:
    
    the 2nd statistical central moment
  - get3rdCentralMoment
```
public final double get3rdCentralMoment()
```
    Returns the 3rd statistical central moment
    
    Returns:
    
    the 3rd statistical central moment
  - get4thCentralMoment
```
public final double get4thCentralMoment()
```
    Returns the 4th statistical central moment
    
    Returns:
    
    the 4th statistical central moment
  - getCentralMoments
```
public final double[] getCentralMoments()
```
    The 0th moment is the count, the 1st central moment zero, the 2nd, 3rd, and 4th central moments
    
    Returns:
    
    an array holding the central moments, 0, 1, 2, 3, 4
  - get2ndRawMoment
```
public final double get2ndRawMoment()
```
    Returns the 2nd statistical raw moment (about zero)
    
    Returns:
    
    the 2nd statistical raw moment (about zero)
  - get3rdRawMoment
```
public final double get3rdRawMoment()
```
    Returns the 3rd statistical raw moment (about zero)
    
    Returns:
    
    the 3rd statistical raw moment (about zero)
  - get4thRawMoment
```
public final double get4thRawMoment()
```
    Returns the 4th statistical raw moment (about zero)
    
    Returns:
    
    the 4th statistical raw moment (about zero)
  - getDeviationSumOfSquares
```
public final double getDeviationSumOfSquares()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the sum of squares of the deviations from the average This is the numerator in the classic sample variance formula
    
    Returns:
    
    A double representing the sum of squares of the deviations from the average
  - getVariance
```
public final double getVariance()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the sample variance of the observations.
    
    Returns:
    
    A double representing the generate variance or Double.NaN if 1 or less observations.
  - getMin
```
public final double getMin()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the minimum of the observations.
    
    Returns:
    
    A double representing the minimum
  - getMax
```
public final double getMax()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the maximum of the observations.
    
    Returns:
    
    A double representing the maximum
  - getLastValue
```
public final double getLastValue()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the last observed data point
    
    Returns:
    
    A double representing the last observations
  - getKurtosis
```
public final double getKurtosis()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the kurtosis of the data
    
    Returns:
    
    A double representing the kurtosis
  - getSkewness
```
public final double getSkewness()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the skewness of the data
    
    Returns:
    
    A double representing the skewness
  - checkMean
```
public final boolean checkMean(double mean)
```
    Checks if the supplied value falls within getAverage() +/- getHalfWidth()
    
    Parameters:
    
    mean - the value to check
    
    Returns:
    
    true if the supplied value falls within getAverage() +/- getHalfWidth()
  - getHalfWidth
```
public double getHalfWidth(double level)
```
    Returns the half-width for a confidence interval on the mean with confidence level based on StudentT distribution
    
    Parameters:
    
    level - the confidence level
    
    Returns:
    
    the half-width
  - getStandardError
```
public final double getStandardError()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the standard error of the observations. Simply the generate standard deviation divided by the square root of the number of observations
    
    Returns:
    
    A double representing the standard error or Double.NaN if < 1 observation
  - getLeadingDigitRule
```
public final int getLeadingDigitRule(double a)
```
    Description copied from interface: StatisticAccessorIfc
    
    Computes the right most meaningful digit according to (int)Math.floor(Math.log10(a*getStandardError())) See doi 10.1287.opre.1080.0529 by Song and Schmeiser
    
    Parameters:
    
    a - the std error multiplier
    
    Returns:
    
    the meaningful digit
  - getLag1Covariance
```
public final double getLag1Covariance()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the lag-1 generate covariance of the unweighted observations. Note: See Box, Jenkins, Reinsel, Time Series Analysis, 3rd edition, Prentice-Hall, pg 31
    
    Returns:
    
    A double representing the generate covariance or Double.NaN if <=2 observations
  - getLag1Correlation
```
public final double getLag1Correlation()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the lag-1 generate correlation of the unweighted observations. Note: See Box, Jenkins, Reinsel, Time Series Analysis, 3rd edition, Prentice-Hall, pg 31
    
    Returns:
    
    A double representing the generate correlation or Double.NaN if <=2 observations
  - getVonNeumannLag1TestStatistic
```
public final double getVonNeumannLag1TestStatistic()
```
    Description copied from interface: StatisticAccessorIfc
    
    Gets the Von Neumann Lag 1 test statistic for checking the hypothesis that the data are uncorrelated Note: See Handbook of Simulation, Jerry Banks editor, McGraw-Hill, pg 253.
    
    Returns:
    
    A double representing the Von Neumann test statistic
  - getVonNeumannLag1TestStatisticPValue
```
public final double getVonNeumannLag1TestStatisticPValue()
```
    Description copied from interface: StatisticAccessorIfc
    
    Returns the asymptotic p-value for the Von Nueumann Lag-1 Test Statistic:
    Normal.stdNormalComplementaryCDF(getVonNeumannLag1TestStatistic());
    
    Returns:
    
    the p-value
  - getObsWeightedSum
```
public final double getObsWeightedSum()
```
    Returns the observation weighted sum of the data i.e. sum = sum + j*x where j is the observation number and x is jth observation
    
    Returns:
    
    the observation weighted sum of the data
  - collect
```
public void collect(double x)
```
    Description copied from interface: CollectorIfc
    
    Collect on the supplied value
    
    Parameters:
    
    x - a double representing the observation
  - reset
```
public void reset()
```
    Description copied from interface: CollectorIfc
    
    Resets the collector as if no observations had been collected.
  - asString
```
public java.lang.String asString()
```
  - toString
```
public java.lang.String toString()
```
    Description copied from interface: StatisticAccessorIfc
    
    Returns a String representation of the Statistic
    
    Specified by:
    
    toString in interface StatisticAccessorIfc
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    A String with basic summary statistics
  - getSummaryStatistics
```
public java.lang.String getSummaryStatistics()
```
    Returns the summary statistics values Name Count Average Std. Dev.
    
    Returns:
    
    the string of summary statistics
  - getSummaryStatisticsHeader
```
public java.lang.String getSummaryStatisticsHeader()
```
    Returns the header for the summary statistics Name Count Average Std. Dev.
    
    Returns:
    
    the header
  - estimateSampleSize
```
public final long estimateSampleSize(double desiredHW)
```
    Estimates the number of observations needed in order to obtain a getConfidenceLevel() confidence interval with plus/minus the provided half-width
    
    Parameters:
    
    desiredHW - the desired half-width, must be greater than zero
    
    Returns:
    
    the estimated sample size
  - estimateSampleSize
```
public static long estimateSampleSize(double desiredHW,
                                      double stdDev,
                                      double level)
```
    Estimate the sample size based on a normal approximation
    
    Parameters:
    
    desiredHW - the desired half-width (must be bigger than 0)
    
    stdDev - the standard deviation (must be bigger than or equal to 0)
    
    level - the confidence level (must be between 0 and 1)
    
    Returns:
    
    the estimated sample size

Class Statistic

Field Summary

Fields inherited from class jsl.utilities.statistic.AbstractStatistic

Fields inherited from interface jsl.utilities.statistic.ArraySaverIfc

Constructor Summary

Method Summary

Methods inherited from class jsl.utilities.statistic.AbstractStatistic

Methods inherited from class jsl.utilities.statistic.AbstractCollector

Methods inherited from class java.lang.Object

Methods inherited from interface jsl.utilities.statistic.CollectorIfc

Methods inherited from interface jsl.utilities.statistic.StatisticAccessorIfc

Field Detail

min

max

num

myValue

sumxx

firstx

moments

myJsum

Constructor Detail

Statistic

Statistic

Statistic

Statistic

Method Detail

collectStatistics

newInstance

newInstance

getIndexOfMin

getMin

getIndexOfMax

getMax

getMedian

countLessEqualTo

countLessThan

countGreaterEqualTo

countGreaterThan

getOrderStatistics

getCount

getSum

getAverage

get2ndCentralMoment

get3rdCentralMoment

get4thCentralMoment

getCentralMoments

get2ndRawMoment

get3rdRawMoment

get4thRawMoment

getDeviationSumOfSquares

getVariance

getMin

getMax

getLastValue

getKurtosis

getSkewness

checkMean

getHalfWidth

getStandardError

getLeadingDigitRule

getLag1Covariance

getLag1Correlation

getVonNeumannLag1TestStatistic

getVonNeumannLag1TestStatisticPValue

getObsWeightedSum

collect

reset

asString

toString

getSummaryStatistics

getSummaryStatisticsHeader

estimateSampleSize

estimateSampleSize