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Statistics Methods

The Statistics type exposes the following members.

Methods
  NameDescription
Public methodStatic memberCovariance(IEnumerableDouble, IEnumerableDouble)
Estimates the unbiased population covariance from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN.
Public methodStatic memberCovariance(IEnumerableNullableDouble, IEnumerableNullableDouble)
Estimates the unbiased population covariance from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberEmpiricalCDF(IEnumerableDouble, Double)
Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
Public methodStatic memberEmpiricalCDF(IEnumerableNullableDouble, Double)
Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
Public methodStatic memberEmpiricalCDFFunc(IEnumerableDouble)
Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
Public methodStatic memberEmpiricalCDFFunc(IEnumerableNullableDouble)
Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
Public methodStatic memberEmpiricalInvCDF(IEnumerableDouble, Double)
Estimates the empirical inverse CDF at tau from the provided samples.
Public methodStatic memberEmpiricalInvCDF(IEnumerableNullableDouble, Double)
Estimates the empirical inverse CDF at tau from the provided samples.
Public methodStatic memberEmpiricalInvCDFFunc(IEnumerableDouble)
Estimates the empirical inverse CDF at tau from the provided samples.
Public methodStatic memberEmpiricalInvCDFFunc(IEnumerableNullableDouble)
Estimates the empirical inverse CDF at tau from the provided samples.
Public methodStatic memberEntropy(IEnumerableDouble)
Calculates the entropy of a stream of double values in bits. Returns NaN if any of the values in the stream are NaN.
Public methodStatic memberEntropy(IEnumerableNullableDouble)
Calculates the entropy of a stream of double values in bits. Returns NaN if any of the values in the stream are NaN. Null-entries are ignored.
Public methodStatic memberFiveNumberSummary(IEnumerableDouble)
Estimates {min, lower-quantile, median, upper-quantile, max} from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberFiveNumberSummary(IEnumerableNullableDouble)
Estimates {min, lower-quantile, median, upper-quantile, max} from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberInterquartileRange(IEnumerableDouble)
Estimates the inter-quartile range from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberInterquartileRange(IEnumerableNullableDouble)
Estimates the inter-quartile range from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberKurtosis(IEnumerableDouble)
Estimates the unbiased population kurtosis from the provided samples. Uses a normalizer (Bessel's correction; type 2). Returns NaN if data has less than four entries or if any entry is NaN.
Public methodStatic memberKurtosis(IEnumerableNullableDouble)
Estimates the unbiased population kurtosis from the provided samples. Uses a normalizer (Bessel's correction; type 2). Returns NaN if data has less than four entries or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberLowerQuartile(IEnumerableDouble)
Estimates the first quartile value from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberLowerQuartile(IEnumerableNullableDouble)
Estimates the first quartile value from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberMaximum(IEnumerableDouble)
Returns the maximum value in the sample data. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberMaximum(IEnumerableNullableDouble)
Returns the maximum value in the sample data. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberMean(IEnumerableDouble)
Evaluates the sample mean, an estimate of the population mean. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberMean(IEnumerableNullableDouble)
Evaluates the sample mean, an estimate of the population mean. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberMeanStandardDeviation
Estimates the sample mean and the unbiased population standard deviation from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN for mean if data is empty or if any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN.
Public methodStatic memberMeanVariance
Estimates the sample mean and the unbiased population variance from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN for mean if data is empty or if any entry is NaN and NaN for variance if data has less than two entries or if any entry is NaN.
Public methodStatic memberMedian(IEnumerableDouble)
Estimates the sample median from the provided samples (R8).
Public methodStatic memberMedian(IEnumerableNullableDouble)
Estimates the sample median from the provided samples (R8).
Public methodStatic memberMinimum(IEnumerableDouble)
Returns the minimum value in the sample data. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberMinimum(IEnumerableNullableDouble)
Returns the minimum value in the sample data. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberMovingAverage
Evaluates the sample mean over a moving window, for each samples. Returns NaN if no data is empty or if any entry is NaN.
Public methodStatic memberOrderStatistic
Returns the order statistic (order 1..N) from the provided samples.
Public methodStatic memberOrderStatisticFunc
Returns the order statistic (order 1..N) from the provided samples.
Public methodStatic memberPercentile(IEnumerableDouble, Int32)
Estimates the p-Percentile value from the provided samples. If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberPercentile(IEnumerableNullableDouble, Int32)
Estimates the p-Percentile value from the provided samples. If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberPercentileFunc(IEnumerableDouble)
Estimates the p-Percentile value from the provided samples. If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberPercentileFunc(IEnumerableNullableDouble)
Estimates the p-Percentile value from the provided samples. If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberPopulationCovariance(IEnumerableDouble, IEnumerableDouble)
Evaluates the population covariance from the provided full populations. On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberPopulationCovariance(IEnumerableNullableDouble, IEnumerableNullableDouble)
Evaluates the population covariance from the provided full populations. On a dataset of size N will use an N normalize and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberPopulationKurtosis(IEnumerableDouble)
Evaluates the kurtosis from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1). Returns NaN if data has less than three entries or if any entry is NaN.
Public methodStatic memberPopulationKurtosis(IEnumerableNullableDouble)
Evaluates the kurtosis from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1). Returns NaN if data has less than three entries or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberPopulationSkewness(IEnumerableDouble)
Evaluates the skewness from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1). Returns NaN if data has less than two entries or if any entry is NaN.
Public methodStatic memberPopulationSkewness(IEnumerableNullableDouble)
Evaluates the skewness from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1). Returns NaN if data has less than two entries or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberPopulationSkewnessKurtosis
Evaluates the skewness and kurtosis from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1).
Public methodStatic memberPopulationStandardDeviation(IEnumerableDouble)
Evaluates the standard deviation from the provided full population. On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberPopulationStandardDeviation(IEnumerableNullableDouble)
Evaluates the standard deviation from the provided full population. On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberPopulationVariance(IEnumerableDouble)
Evaluates the variance from the provided full population. On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberPopulationVariance(IEnumerableNullableDouble)
Evaluates the variance from the provided full population. On a dataset of size N will use an N normalize and would thus be biased if applied to a subsetr. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberQuantile(IEnumerableDouble, Double)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberQuantile(IEnumerableNullableDouble, Double)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberQuantileCustom(IEnumerableDouble, Double, QuantileDefinition)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileCustom(IEnumerableNullableDouble, Double, QuantileDefinition)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileCustomFunc(IEnumerableDouble, QuantileDefinition)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileCustomFunc(IEnumerableNullableDouble, QuantileDefinition)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileFunc(IEnumerableDouble)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberQuantileFunc(IEnumerableNullableDouble)
Estimates the tau-th quantile from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberQuantileRank(IEnumerableDouble, Double, RankDefinition)
Estimates the quantile tau from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileRank(IEnumerableNullableDouble, Double, RankDefinition)
Estimates the quantile tau from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileRankFunc(IEnumerableDouble, RankDefinition)
Estimates the quantile tau from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberQuantileRankFunc(IEnumerableNullableDouble, RankDefinition)
Estimates the quantile tau from the provided samples. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
Public methodStatic memberRanks(IEnumerableDouble, RankDefinition)
Evaluates the rank of each entry of the provided samples. The rank definition can be specified to be compatible with an existing system.
Public methodStatic memberRanks(IEnumerableNullableDouble, RankDefinition)
Evaluates the rank of each entry of the provided samples. The rank definition can be specified to be compatible with an existing system.
Public methodStatic memberRootMeanSquare(IEnumerableDouble)
Evaluates the root mean square (RMS) also known as quadratic mean. Returns NaN if data is empty or if any entry is NaN.
Public methodStatic memberRootMeanSquare(IEnumerableNullableDouble)
Evaluates the root mean square (RMS) also known as quadratic mean. Returns NaN if data is empty or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberSkewness(IEnumerableDouble)
Estimates the unbiased population skewness from the provided samples. Uses a normalizer (Bessel's correction; type 2). Returns NaN if data has less than three entries or if any entry is NaN.
Public methodStatic memberSkewness(IEnumerableNullableDouble)
Estimates the unbiased population skewness from the provided samples. Uses a normalizer (Bessel's correction; type 2). Returns NaN if data has less than three entries or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberSkewnessKurtosis
Estimates the unbiased population skewness and kurtosis from the provided samples in a single pass. Uses a normalizer (Bessel's correction; type 2).
Public methodStatic memberStandardDeviation(IEnumerableDouble)
Estimates the unbiased population standard deviation from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN.
Public methodStatic memberStandardDeviation(IEnumerableNullableDouble)
Estimates the unbiased population standard deviation from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN. Null-entries are ignored.
Public methodStatic memberUpperQuartile(IEnumerableDouble)
Estimates the third quartile value from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberUpperQuartile(IEnumerableNullableDouble)
Estimates the third quartile value from the provided samples. Approximately median-unbiased regardless of the sample distribution (R8).
Public methodStatic memberVariance(IEnumerableDouble)
Estimates the unbiased population variance from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN.
Public methodStatic memberVariance(IEnumerableNullableDouble)
Estimates the unbiased population variance from the provided samples. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN. Null-entries are ignored.
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