StatisticIfc

The StatisticIfc interface presents a read-only view of a Statistic

Inheritors

Properties

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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()

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open val asStrings: List<String>

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

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abstract val average: Double

Gets the unweighted average of the observations.

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A confidence interval for the mean based on the confidence level

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abstract val confidenceLevel: Double

Gets the confidence level. The default is given by Statistic.DEFAULT_CONFIDENCE_LEVEL = 0.95, which is a 95% confidence level

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abstract val count: Double

Gets the count of the number of the observations.

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open val csvHeader: List<String>

Gets the CSV header values as a list of strings

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open override val csvStatistic: String
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open override val csvStatisticHeader: String

The header string for the CVS representation

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

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open val halfWidth: Double

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

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abstract val kurtosis: Double

Gets the kurtosis of the data

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abstract val lag1Correlation: Double

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

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abstract val lag1Covariance: Double

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

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abstract val lastValue: Double
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abstract val max: Double

Gets the maximum of the observations.

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abstract val min: Double

Gets the minimum of the observations.

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abstract override val name: String

Gets the name of the Statistic

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abstract val negativeCount: Double

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

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abstract val numberMissing: Double

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

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

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

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

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abstract val skewness: Double

Gets the skewness of the data

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

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abstract val standardError: Double

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

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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"

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abstract val sum: Double

Gets the sum of the observations.

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abstract val value: Double
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abstract val variance: Double

Gets the sample variance of the observations.

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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.

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

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open val width: Double
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abstract val zeroCount: Double

Counts the number of observations that were exactly zero.

Functions

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A confidence interval for the mean based on the confidence level

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

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open override fun estimate(): Double
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open fun halfWidth(level: Double): Double

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

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open fun leadingDigitRule(multiplier: Double): Int

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

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open fun relativeWidth(level: Double): Double

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

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open fun statisticData(level: Double = 0.95): StatisticData

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

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open fun statisticDataDb(level: Double = 0.95, context: String? = null, subject: String? = null, tableName: String = "tblStatistic"): StatisticDataDb

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.

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fun StatisticIfc.toStatDataFrame(valueLabel: String = "Value"): DataFrame<StatSchema>

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".

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abstract override fun toString(): String

Returns a String representation of the Statistic

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open fun width(level: Double): Double