Statistics
StandardDeviation
compute the standard deviation
Calling Sequence
Parameters
Description
Computation
Data Set Options
Random Variable Options
Examples
References
Compatibility
StandardDeviation(A, ds_options)
StandardDeviation(X, rv_options)
A
-
data set or Matrix data set
X
algebraic; random variable or distribution
ds_options
(optional) equation(s) of the form option=value where option is one of ignore, or weights; specify options for computing the standard deviation of a data set
rv_options
(optional) equation of the form numeric=value; specifies options for computing the standard deviation of a random variable
The StandardDeviation function computes the standard deviation of the specified data set or random variable. In the data set case the unbiased estimate for the variance is used (see Statistics,Variance for more details).
The first parameter can be a data set, a distribution (see Statistics[Distribution]), a random variable, or an algebraic expression involving random variables (see Statistics[RandomVariable]).
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.
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 StandardDeviation command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the StandardDeviation 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.
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 standard deviation is computed using exact arithmetic. To compute the standard deviation numerically, specify the numeric or numeric = true option.
with⁡Statistics:
Compute the standard deviation of the beta distribution with parameters p and q.
StandardDeviation⁡Β⁡p,q
p⁢qp+q+1p+q
Use numeric parameters.
StandardDeviation⁡Β⁡3,5
1524
StandardDeviation⁡Β⁡3,5,numeric
0.1613743061
Generate a random sample of size 100000 drawn from the above distribution and compute the sample standard deviation.
A≔Sample⁡Β⁡3,5,105:
StandardDeviation⁡A
0.161964889308041
Compute the standard error of the sample standard deviation for the normal distribution with parameters 5 and 2.
X≔RandomVariable⁡Normal⁡5,2
X≔_R3
B≔Sample⁡X,106:
StandardDeviation⁡X,StandardError106⁡StandardDeviation,X
2,21000
StandardDeviation⁡B
1.99975291418549
Create a beta-distributed random variable Y and compute the standard deviation of 1Y+2.
Y≔RandomVariable⁡Β⁡5,2:
StandardDeviation⁡1Y+2
−1356439+16588800⁢ln⁡3⁢ln⁡2−8294400⁢ln⁡22−6708480⁢ln⁡2−8294400⁢ln⁡32+6708480⁢ln⁡32
StandardDeviation⁡1Y+2,numeric
0.02274855629
Verify this using simulation.
C≔Sample⁡1Y+2,105:
StandardDeviation⁡C
0.0227599052735762
Compute the standard deviation 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:
StandardDeviation⁡V,weights=W
Float⁡undefined
StandardDeviation⁡V,weights=W,ignore=true
2.72742139848191
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 standard deviation of each of the columns.
StandardDeviation⁡M
0.836660026534076264.57191838893317953.9731201759
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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