Statistics
Moment
compute moments
Calling Sequence
Parameters
Description
Computation
Data Set Options
Random Variable Options
Examples
References
Compatibility
Moment(A, n, ds_options)
Moment(X, n, rv_options)
A
-
data set or Matrix data set
X
algebraic; random variable or distribution
n
algebraic; order
ds_options
(optional) equation(s) of the form option=value where option is one of ignore, origin, or weights; specify options for computing the moment of a data set
rv_options
(optional) equation(s) of the form option=value where option is one of numeric or origin; specifies options for computing the moment of a random variable
The Moment function computes the moment of order n of the specified random variable or data set.
The first parameter can be a data set (e.g., a Vector), a Matrix data set, a distribution (see Statistics[Distribution]), a random variable, or an algebraic expression involving random variables (see Statistics[RandomVariable]).
The second parameter can be any Maple expression.
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.
By default, all computations involving random variables are performed symbolically (see option numeric below).
For more information about computation in the Statistics package, see the Statistics[Computation] help page.
The ds_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 Moment command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the Moment 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 1.
origin=algebraic -- By default, the moment is computed about 0. If this option is present, the moment will be calculated about the specified point. If A is a Matrix, then you can specify several origins instead, one for each column of the matrix. This is accomplished by passing a list or Vector as the value of the origin option.
The rv_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[RandomVariables] help page.
numeric=truefalse -- By default, the moment is computed symbolically. To compute the moment numerically, specify the numeric or numeric = true option.
origin=algebraic -- By default, the moment is computed about 0. If this option is present, the moment will be calculated about the specified point.
with⁡Statistics:
Compute the third moment of the beta distribution with parameters 3 and 5.
Moment⁡Β⁡3,5,3
112
Moment⁡Β⁡3,5,3,numeric
0.08333333333
Moment⁡Β⁡3,5,3,origin=12
−196
Moment⁡Β⁡3,5,3,origin=12,numeric
−0.01041666667
Generate a random sample of size 100000 drawn from the above distribution and compute the third moment.
A≔Sample⁡Β⁡3,5,105:
Moment⁡A,3
0.0833620461074444
Moment⁡A,3,origin=12
−0.0105063541487697
Compute the standard error of the third moment for the normal distribution with parameters 5 and 2.
X≔Normal⁡5,2
B≔Sample⁡X,106:
Moment⁡X,3,StandardError106⁡Moment,X,3,origin=0
185,9465500
Moment⁡X,3,numeric,StandardError106⁡Moment,X,3,origin=0,numeric
185.,0.1945764631
Moment⁡B,3,StandardError⁡Moment,B,3,origin=0
184.777649882669,0.194465203777126283
Create a beta-distributed random variable Y and compute the third moment of 1Y+2.
Y≔RandomVariable⁡Β⁡5,2:
Moment⁡1Y+2,3,numeric
0.05113729747
Verify this using simulation.
C≔Sample⁡1Y+2,105:
Moment⁡C,3
0.0511287210305800
Compute the average moment of a weighted data set.
V≔seq⁡i,i=57..77,undefined:
W≔2,4,14,41,83,169,394,669,990,1223,1329,1230,1063,646,392,202,79,32,16,5,2,5:
Moment⁡V,3,weights=W
Float⁡undefined
Moment⁡V,3,weights=W,ignore=true
302374.062434479
Consider the following Matrix data set.
M≔Matrix⁡3,1130,114694,4,1527,127368,3,907,88464,2,878,96484,4,995,128007
M≔31130114694415271273683907884642878964844995128007
We compute the second moment of each of the columns.
Moment⁡M,2
10.80000000000001.23843740000000×1061.25796309322000×1010
We compute the second moment of each column with origin 3.
Moment⁡M,2,origin=3
0.6000000000000001.231922×1061.25789649208000×1010
We compute the second moment of each column with three different origins.
Moment⁡M,2,origin=3,1000,100000
0.60000000000000063637.40000000003.78950932200000×108
Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998. Vol. 1: Distribution Theory.
The A parameter was updated in Maple 16.
See Also
Statistics[Computation]
Statistics[DescriptiveStatistics]
Statistics[Distributions]
Statistics[ExpectedValue]
Statistics[RandomVariables]
Statistics[StandardError]
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