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Statistics[Correlation] - compute the correlation/correlation matrix
Calling Sequence
Correlation(X, Y, options)
CorrelationMatrix(M, options)
Parameters
M
-
Matrix; data samples
X
Vector; data set
Y
options
(optional) equation(s) of the form option=value where option is one of ignore, or weights; specify options for computing the correlation/correlation matrix
Description
The Correlation function computes the correlation of two data sets. The CorrelationMatrix function computes the correlation matrix of multiple data sets.
The first parameter can be a data set (represented as an Array), a distribution (see Statistics[Distribution]), a random variable, or an algebraic expression involving random variables (see Statistics[RandomVariable]).
Computation
By default, all computations involving random variables are performed symbolically (see option numeric below).
All computations involving data are performed in floating-point; therefore, all data provided must have type realcons and all returned solutions are floating-point, even if the problem is specified with exact values.
For more information about computation in the Statistics package, see the Statistics[Computation] help page.
Options
The options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[DescriptiveStatistics] help page.
ignore=truefalse -- This option controls how missing data is handled by the Correlation command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the Correlation command will return undefined. If ignore=true all missing items in A will be ignored. The default value is false.
weights=Vector -- Data weights. The number of elements in the weights array must be equal to the number of elements in the original data sample. By default all elements in A are assigned weight .
Examples
See Also
Statistics, Statistics[Computation], Statistics[DescriptiveStatistics]
References
Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998. Vol. 1: Distribution Theory.
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