All Superinterfaces:

GetCSVStatisticIfc

All Known Implementing Classes:

AbstractStatistic, AntitheticStatistic, BatchStatistic, CachedHistogram, Histogram, IntermittentDemandStatistic, StandardizedTimeSeriesStatistic, Statistic
```
public interface StatisticAccessorIfc
extends GetCSVStatisticIfc
```
The StatisticAccessIfc class presents a read-only view of a Statistic

Method Summary

All Methods Instance Methods Abstract Methods Default Methods
Modifier and Type	Method	Description
`double`	`getAverage()`	Gets the unweighted average of the observations.
`default Interval`	`getConfidenceInterval()`	A confidence interval for the mean based on the confidence level
`default Interval`	`getConfidenceInterval(double level)`	A confidence interval for the mean based on the confidence level
`double`	`getConfidenceLevel()`	Gets the confidence level.
`double`	`getCount()`	Gets the count of the number of the observations.
`default java.util.List<java.lang.String>`	`getCSVHeader()`	Gets the CSV header values as a list of strings
`default java.lang.String`	`getCSVStatistic()`
`default java.lang.String`	`getCSVStatisticHeader()`	The header string for the CVS representation
`default java.util.List<java.lang.String>`	`getCSVValues()`	Returns the values of all the statistics as a list of strings The name is the first string
`double`	`getDeviationSumOfSquares()`	Gets the sum of squares of the deviations from the average This is the numerator in the classic sample variance formula
`default double`	`getHalfWidth()`	Gets the confidence interval half-width.
`double`	`getHalfWidth(double level)`	Gets the confidence interval half-width.
`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.
`double`	`getMin()`	Gets the minimum of the observations.
`java.lang.String`	`getName()`	Gets the name of the Statistic
`double`	`getNumberMissing()`	When a data point having the value of (Double.NaN, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY) are presented it is excluded from the summary statistics and the number of missing points is noted.
`default double`	`getRelativeError()`	Returns the relative error: getStandardError() / getAverage()
`default double`	`getRelativeWidth()`	Returns the relative width of the default confidence interval: 2.0 * getHalfWidth() / getAverage()
`default double`	`getRelativeWidth(double level)`	Returns the relative width of the level of the confidence interval: 2.0 * getHalfWidth(level) / getAverage()
`double`	`getSkewness()`	Gets the skewness of the data
`default double`	`getStandardDeviation()`	Gets the sample standard deviation of the observations.
`double`	`getStandardError()`	Gets the standard error of the observations.
`default double[]`	`getStatistics()`	Fills up an array with the statistics defined by this interface statistics[0] = getCount() statistics[1] = getAverage() statistics[2] = getStandardDeviation() statistics[3] = getStandardError() statistics[4] = getHalfWidth() statistics[5] = getConfidenceLevel() statistics[6] = getMin() statistics[7] = getMax() statistics[8] = getSum() statistics[9] = getVariance() statistics[10] = getDeviationSumOfSquares() statistics[11] = getLastValue() statistics[12] = getKurtosis() statistics[13] = getSkewness() statistics[14] = getLag1Covariance() statistics[15] = getLag1Correlation() statistics[16] = getVonNeumannLag1TestStatistic() statistics[17] = getNumberMissing()
`default java.util.Map<java.lang.String,java.lang.Double>`	`getStatisticsAsMap()`	Fills the map with the values of the statistics.
`double`	`getSum()`	Gets the sum of the observations.
`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:
`java.lang.String`	`toString()`	Returns a String representation of the Statistic

- Method Detail
  - getName
```
java.lang.String getName()
```
    Gets the name of the Statistic
    
    Returns:
    
    The name as a String
  - getCount
```
double getCount()
```
    Gets the count of the number of the observations.
    
    Returns:
    
    A double representing the count
  - getSum
```
double getSum()
```
    Gets the sum of the observations.
    
    Returns:
    
    A double representing the unweighted sum
  - getAverage
```
double getAverage()
```
    Gets the unweighted average of the observations.
    
    Returns:
    
    A double representing the average or Double.NaN if no observations.
  - getDeviationSumOfSquares
```
double getDeviationSumOfSquares()
```
    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
```
double getVariance()
```
    Gets the sample variance of the observations.
    
    Returns:
    
    A double representing the generate variance or Double.NaN if 1 or less observations.
  - getStandardDeviation
```
default double getStandardDeviation()
```
    Gets the sample standard deviation of the observations. Simply the square root of getVariance()
    
    Returns:
    
    A double representing the generate standard deviation or Double.NaN if 1 or less observations.
  - getMin
```
double getMin()
```
    Gets the minimum of the observations.
    
    Returns:
    
    A double representing the minimum
  - getMax
```
double getMax()
```
    Gets the maximum of the observations.
    
    Returns:
    
    A double representing the maximum
  - getLastValue
```
double getLastValue()
```
    Gets the last observed data point
    
    Returns:
    
    A double representing the last observations
  - getKurtosis
```
double getKurtosis()
```
    Gets the kurtosis of the data
    
    Returns:
    
    A double representing the kurtosis
  - getSkewness
```
double getSkewness()
```
    Gets the skewness of the data
    
    Returns:
    
    A double representing the skewness
  - getStandardError
```
double getStandardError()
```
    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
  - getHalfWidth
```
default double getHalfWidth()
```
    Gets the confidence interval half-width. Simply the generate standard error times the confidence coefficient
    
    Returns:
    
    A double representing the half-width or Double.NaN if < 1 observation
  - getHalfWidth
```
double getHalfWidth(double level)
```
    Gets the confidence interval half-width. Simply the generate standard error times the confidence coefficient as determined by an appropriate sampling distribution
    
    Parameters:
    
    level - the confidence level
    
    Returns:
    
    A double representing the half-width or Double.NaN if < 1 observation
  - getConfidenceLevel
```
double getConfidenceLevel()
```
    Gets the confidence level. The default is given by Statistic.DEFAULT_CONFIDENCE_LEVEL = 0.95, which is a 95% confidence level
    
    Returns:
    
    A double representing the confidence level
  - getConfidenceInterval
```
default Interval getConfidenceInterval()
```
    A confidence interval for the mean based on the confidence level
    
    Returns:
    
    the interval
  - getConfidenceInterval
```
default Interval getConfidenceInterval(double level)
```
    A confidence interval for the mean based on the confidence level
    
    Parameters:
    
    level - the confidence level
    
    Returns:
    
    the interval
  - getRelativeError
```
default double getRelativeError()
```
    Returns the relative error: getStandardError() / getAverage()
    
    Returns:
    
    the relative error
  - getRelativeWidth
```
default double getRelativeWidth()
```
    Returns the relative width of the default confidence interval: 2.0 * getHalfWidth() / getAverage()
    
    Returns:
    
    the relative width
  - getRelativeWidth
```
default double getRelativeWidth(double level)
```
    Returns the relative width of the level of the confidence interval: 2.0 * getHalfWidth(level) / getAverage()
    
    Parameters:
    
    level - the confidence level
    
    Returns:
    
    the relative width for the level
  - getLag1Covariance
```
double getLag1Covariance()
```
    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
```
double getLag1Correlation()
```
    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
```
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.
    
    Returns:
    
    A double representing the Von Neumann test statistic
  - getVonNeumannLag1TestStatisticPValue
```
double getVonNeumannLag1TestStatisticPValue()
```
    Returns the asymptotic p-value for the Von Nueumann Lag-1 Test Statistic:
    Normal.stdNormalComplementaryCDF(getVonNeumannLag1TestStatistic());
    
    Returns:
    
    the p-value
  - getNumberMissing
```
double getNumberMissing()
```
    When a data point having the value of (Double.NaN, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY) are presented it is excluded from the summary statistics and the number of missing points is noted. This method reports the number of missing points that occurred during the collection
    
    Returns:
    
    the number missing
  - getLeadingDigitRule
```
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
    
    Parameters:
    
    a - the std error multiplier
    
    Returns:
    
    the meaningful digit
  - toString
```
java.lang.String toString()
```
    Returns a String representation of the Statistic
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    A String with basic summary statistics
  - getStatistics
```
default double[] getStatistics()
```
    Fills up an array with the statistics defined by this interface statistics[0] = getCount() statistics[1] = getAverage() statistics[2] = getStandardDeviation() statistics[3] = getStandardError() statistics[4] = getHalfWidth() statistics[5] = getConfidenceLevel() statistics[6] = getMin() statistics[7] = getMax() statistics[8] = getSum() statistics[9] = getVariance() statistics[10] = getDeviationSumOfSquares() statistics[11] = getLastValue() statistics[12] = getKurtosis() statistics[13] = getSkewness() statistics[14] = getLag1Covariance() statistics[15] = getLag1Correlation() statistics[16] = getVonNeumannLag1TestStatistic() statistics[17] = getNumberMissing()
    
    Returns:
    
    an array of values
  - getCSVStatisticHeader
```
default java.lang.String getCSVStatisticHeader()
```
    Description copied from interface: GetCSVStatisticIfc
    
    The header string for the CVS representation
    
    Specified by:
    
    getCSVStatisticHeader in interface GetCSVStatisticIfc
    
    Returns:
    
    the CVS header string
  - getCSVStatistic
```
default java.lang.String getCSVStatistic()
```
    Specified by:
    
    getCSVStatistic in interface GetCSVStatisticIfc
    
    Returns:
    
    the CSV string for the values of the statistics
  - getCSVValues
```
default java.util.List<java.lang.String> getCSVValues()
```
    Returns the values of all the statistics as a list of strings The name is the first string
    
    Returns:
    
    the values of all the statistics as a list of strings
  - getCSVHeader
```
default java.util.List<java.lang.String> getCSVHeader()
```
    Gets the CSV header values as a list of strings
    
    Returns:
    
    the CSV header values as a list of strings
  - getStatisticsAsMap
```
default java.util.Map<java.lang.String,java.lang.Double> getStatisticsAsMap()
```
    Fills the map with the values of the statistics. Key is statistic label and value is the value of the statistic. The keys are: "Count" "Average" "Standard Deviation" "Standard Error" "Half-width" "Confidence Level" "Minimum" "Maximum" "Sum" "Variance" "Deviation Sum of Squares" "Last value collected" "Kurtosis" "Skewness" "Lag 1 Covariance" "Lag 1 Correlation" "Von Neumann Lag 1 Test Statistic" "Number of missing observations"

Interface StatisticAccessorIfc

Method Summary

Method Detail

getName

getCount

getSum

getAverage

getDeviationSumOfSquares

getVariance

getStandardDeviation

getMin

getMax

getLastValue

getKurtosis

getSkewness

getStandardError

getHalfWidth

getHalfWidth

getConfidenceLevel

getConfidenceInterval

getConfidenceInterval

getRelativeError

getRelativeWidth

getRelativeWidth

getLag1Covariance

getLag1Correlation

getVonNeumannLag1TestStatistic

getVonNeumannLag1TestStatisticPValue

getNumberMissing

getLeadingDigitRule

toString

getStatistics

getCSVStatisticHeader

getCSVStatistic

getCSVValues

getCSVHeader

getStatisticsAsMap