AbstractStatistic

Fills up an array with the statistics defined by this interface statistics0 = getCount() statistics1 = getAverage() statistics2 = getStandardDeviation() statistics3 = getStandardError() statistics4 = getHalfWidth() statistics5 = getConfidenceLevel() statistics6 = getMin() statistics7 = getMax() statistics8 = getSum() statistics9 = getVariance() statistics10 = getDeviationSumOfSquares() statistics11 = getLastValue() statistics12 = getKurtosis() statistics13 = getSkewness() statistics14 = getLag1Covariance() statistics15 = getLag1Correlation() statistics16 = getVonNeumannLag1TestStatistic() statistics17 = getNumberMissing()

Returns the values of all the statistics as a list of strings The name is the first string

Gets the unweighted average of the observations.

A confidence interval for the mean based on the confidence level

Holds the confidence coefficient for the statistic

Gets the count of the number of the observations.

Gets the CSV header values as a list of strings

The header string for the CVS representation

Gets the sum of squares of the deviations from the average This is the numerator in the classic sample variance formula

Gets the confidence interval half-width. Simply the standard error times the confidence coefficient

Gets the kurtosis of the data

Gets the lag-1 generate correlation of the unweighted observations. Note: See Box, Jenkins, Reinsel, Time Series Analysis, 3rd edition, Prentice-Hall, pg 31

Gets the lag-1 generate covariance of the unweighted observations. Note: See Box, Jenkins, Reinsel, Time Series Analysis, 3rd edition, Prentice-Hall, pg 31

Gets the maximum of the observations.

Gets the minimum of the observations.

Counts the number of observations that were negative, strictly less than zero.

Used to count the number of missing data points presented 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. Implementers of subclasses are responsible for properly collecting this value and resetting this value.

Counts the number of observations that were positive, strictly greater than zero.

Returns the relative error: getStandardError() / getAverage()

Returns the relative width of the default confidence interval: 2.0 * getHalfWidth() / getAverage()

Gets the skewness of the data

Gets the sample standard deviation of the observations. Simply the square root of variance

Gets the standard error of the observations. Simply the generate standard deviation divided by the square root of the number of observations

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" "Lower Limit" "Upper Limit" "Minimum" "Maximum" "Sum" "Variance" "Deviation Sum of Squares" "Kurtosis" "Skewness" "Lag 1 Covariance" "Lag 1 Correlation" "Von Neumann Lag 1 Test Statistic" "Number of missing observations"

Gets the sum of the observations.

Gets the sample variance of the observations.

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 the asymptotic p-value for the Von Nueumann Lag-1 Test Statistic:

Counts the number of observations that were exactly zero.

Allows the adding (attaching) of an observer to the observable

Collects on the boolean value true = 1.0, false = 0.0

Collects on the values in the supplied array.

Collect on the supplied value. Double.NaN, Double.NEGATIVE_INFINITY, and Double.POSITIVE_INFINITY values are counted as missing. Null values are not permitted.

Collect on the double value return by the function

Collects on the Int value

Collects on the Long value

Collects on all the values in the supplied collection.

Collects on the values returned by the supplied GetValueIfc

Returns a negative integer, zero, or a positive integer if this object is less than, equal to, or greater than the specified object.

A confidence interval for the mean based on the confidence level

Return a copy of the information as an instance of a statistic

Returns how many observers are currently attached to the observable

Detaches all the observers from the observable

Allows the deletion (removing) of an observer from the observable

Gets the confidence interval half-width. Simply the standard error times the confidence coefficient as determined by an appropriate sampling distribution

Returns true if the observer is already attached

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

Returns the relative width of the level of the confidence interval: 2.0 * getHalfWidth(level) / getAverage()

Resets the collector as if no observations had been collected.

Returns a data class holding the statistical data with the confidence interval specified by the given level.

Returns a data class holding the statistical data with the confidence interval specified by the given level. The class is suitable for inserting into a database table.

Converts a statistic to a data frame with two columns. The first column holds the names of the statistics and the second column holds the values. The valueLabel can be used to provide a column name for the value columns. By default, it is "Value".