Statistic
The Statistic class allows the collection of summary statistics on data via the collect() methods. The primary statistical summary is for the statistical moments. Creates a Statistic with the given name
Parameters
an optional String representing the name of the statistic
an optional array of values to collect on
Properties
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 2nd statistical central moment
Returns the 3rd statistical central moment
Returns the 4th statistical central moment
The 0th moment is the count, the 1st central moment zero, the 2nd, 3rd, and 4th central moments
A confidence interval for the mean based on the confidence level
Holds the confidence coefficient for the statistic
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 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
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.
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
Counts the number of observations that were positive, strictly greater than zero.
Returns the 2nd statistical raw moment (about zero)
Returns the 3rd statistical raw moment (about zero)
Returns the 4th statistical raw moment (about zero)
Returns the relative error: getStandardError() / getAverage()
Returns the relative width of the default confidence interval: 2.0 * getHalfWidth() / getAverage()
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"
Returns the summary statistics values Name Count Average Std. Dev.
Returns the header for the summary statistics Name Count Average Std. Dev.
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:
Functions
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 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
Collect on the supplied value. Double.NaN, Double.NEGATIVE_INFINITY, and Double.POSITIVE_INFINITY values are counted as missing. Null values are not permitted.
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
Estimates the number of observations needed in order to obtain a getConfidenceLevel() confidence interval with plus/minus the provided half-width
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()
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".