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Summary Class
Computes basic univariate statistics.
Inheritance Hierarchy
SystemObject
  Imsl.StatSummary

Namespace: Imsl.Stat
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0
Syntax
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[SerializableAttribute]
public class Summary

The Summary type exposes the following members.

Constructors
  NameDescription
Public methodSummary
Constructs a new summary statistics object.
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Methods
  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetConfidenceMean
Returns the confidence interval for the mean (assuming normality).
Public methodGetConfidenceVariance
Returns the confidence interval for the variance (assuming normality).
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetKurtosis
Returns the kurtosis.
Public methodStatic memberGetKurtosis(Double)
Returns the kurtosis of the given data set.
Public methodStatic memberGetKurtosis(Double, Double)
Returns the kurtosis of the given data set and associated weights.
Public methodGetMaximum
Returns the maximum.
Public methodStatic memberGetMaximum(Double)
Returns the maximum of the given data set.
Protected methodStatic memberGetMaximum(Int32)
Returns the maximum of the given data set.
Public methodGetMean
Returns the population mean.
Public methodStatic memberGetMean(Double)
Returns the mean of the given data set.
Public methodStatic memberGetMean(Double, Double)
Returns the mean of the given data set with associated weights.
Public methodStatic memberGetMedian
Returns the median of the given data set.
Public methodGetMinimum
Returns the minimum.
Public methodStatic memberGetMinimum(Double)
Returns the minimum of the given data set.
Protected methodStatic memberGetMinimum(Int32)
Returns the minimum of the given data set.
Public methodStatic memberGetMode
Returns the mode of the given data set.
Public methodGetSampleStandardDeviation
Returns the sample standard deviation.
Public methodStatic memberGetSampleStandardDeviation(Double)
Returns the sample standard deviation of the given data set.
Public methodStatic memberGetSampleStandardDeviation(Double, Double)
Returns the sample standard deviation of the given data set and associated weights.
Public methodGetSampleVariance
Returns the sample variance.
Public methodStatic memberGetSampleVariance(Double)
Returns the sample variance of the given data set.
Public methodStatic memberGetSampleVariance(Double, Double)
Returns the sample variance of the given data set and associated weights.
Public methodGetSkewness
Returns the skewness.
Public methodStatic memberGetSkewness(Double)
Returns the skewness of the given data set.
Public methodStatic memberGetSkewness(Double, Double)
Returns the skewness of the given data set and associated weights.
Public methodGetStandardDeviation
Returns the population standard deviation.
Public methodStatic memberGetStandardDeviation(Double)
Returns the population standard deviation of the given data set.
Public methodStatic memberGetStandardDeviation(Double, Double)
Returns the population standard deviation of the given data set and associated weights.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodGetVariance
Returns the population variance.
Public methodStatic memberGetVariance(Double)
Returns the population variance of the given data set.
Public methodStatic memberGetVariance(Double, Double)
Returns the population variance of the given data set and associated weights.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Public methodUpdate(Double)
Adds an observation to the Summary object.
Public methodUpdate(Double)
Adds a set of observations to the Summary object.
Public methodUpdate(Double, Double)
Adds an observation and associated weight to the Summary object.
Public methodUpdate(Double, Double)
Adds a set of observations and associated weights to the Summary object.
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Remarks

For the data in x, Summary computes the sample mean, variance, minimum, maximum, and other basic statistics. It also computes confidence intervals for the mean and variance if the sample is assumed to be from a normal population.

Missing values, that is, values equal to NaN (not a number), are excluded from the computations. The sum of the weights is used only in computing the mean (of course, then the weighted mean is used in computing the central moments). The definitions of some of the statistics are given below in terms of a single variable x. The i-th datum is x_i, with corresponding weight w_i. If weights are not specified, the w_i are identically one. The summation in each case is over the set of valid observations, based on the presence of missing values in the data.

Number of nonmissing observations,

n = \sum {f_i }

Mean,

\bar x_w = \frac{{\sum {f_i w_i x_i } }}{{\sum {f_i w_i } }}

Variance,

s_w^2 = \frac{{\sum {f_i w_i \left( {x_i - \bar x_w } \right)^2 } }}{{n - 1}}

Skewness,

\frac{{\sum {f_i w_i \left( {x_i - \bar x_w } \right)^3 /n} }}{{[\sum {f_i w_i \left( {x_i - \bar x_w } \right)^2 /n]^{3/2} } }}

Excess or Kurtosis,

\frac{{\sum {f_i w_i \left( {x_i - \bar x_w } \right)^4 /n} }}{{[\sum {f_i w_i \left( {x_i - \bar x_w } \right)^2 /n]^2 } }} - 3

Minimum,

x_{\rm min} = \min (x_i )

Maximum,

x_{\rm max} = \max (x_i )

See Also

Reference

Imsl.Stat Namespace

Other Resources

Example