Summary Class |
Namespace: Imsl.Stat
The Summary type exposes the following members.
Name | Description | |
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Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |
GetConfidenceMean |
Returns the confidence interval for the mean (assuming normality).
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GetConfidenceVariance |
Returns the confidence interval for the variance (assuming normality).
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GetHashCode | Serves as a hash function for a particular type. (Inherited from Object.) | |
GetKurtosis |
Returns the kurtosis.
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GetKurtosis(Double) |
Returns the kurtosis of the given data set.
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GetKurtosis(Double, Double) |
Returns the kurtosis of the given data set and associated weights.
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GetMaximum |
Returns the maximum.
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GetMaximum(Double) |
Returns the maximum of the given data set.
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GetMaximum(Int32) |
Returns the maximum of the given data set.
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GetMean |
Returns the population mean.
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GetMean(Double) |
Returns the mean of the given data set.
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GetMean(Double, Double) |
Returns the mean of the given data set with associated weights.
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GetMedian |
Returns the median of the given data set.
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GetMinimum |
Returns the minimum.
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GetMinimum(Double) |
Returns the minimum of the given data set.
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GetMinimum(Int32) |
Returns the minimum of the given data set.
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GetMode |
Returns the mode of the given data set.
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GetSampleStandardDeviation |
Returns the sample standard deviation.
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GetSampleStandardDeviation(Double) |
Returns the sample standard deviation of the given data set.
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GetSampleStandardDeviation(Double, Double) |
Returns the sample standard deviation of the given data set and
associated weights.
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GetSampleVariance |
Returns the sample variance.
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GetSampleVariance(Double) |
Returns the sample variance of the given data set.
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GetSampleVariance(Double, Double) |
Returns the sample variance of the given data set and associated
weights.
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GetSkewness |
Returns the skewness.
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GetSkewness(Double) |
Returns the skewness of the given data set.
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GetSkewness(Double, Double) |
Returns the skewness of the given data set and associated weights.
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GetStandardDeviation |
Returns the population standard deviation.
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GetStandardDeviation(Double) |
Returns the population standard deviation of the given data set.
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GetStandardDeviation(Double, Double) |
Returns the population standard deviation of the given data set and
associated weights.
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GetType | Gets the Type of the current instance. (Inherited from Object.) | |
GetVariance |
Returns the population variance.
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GetVariance(Double) |
Returns the population variance of the given data set.
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GetVariance(Double, Double) |
Returns the population variance of the given data set and associated
weights.
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MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |
ToString | Returns a string that represents the current object. (Inherited from Object.) | |
Update(Double) |
Adds an observation to the Summary object.
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Update(Double) |
Adds a set of observations to the Summary object.
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Update(Double, Double) |
Adds an observation and associated weight to the Summary
object.
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Update(Double, Double) |
Adds a set of observations and associated weights to the
Summary object.
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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 , with corresponding weight . If weights are not specified, the 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,
Mean,
Variance,
Skewness,
Excess or Kurtosis,
Minimum,
Maximum,