Statistics[StandardError] - estimate standard error of a sampling distribution
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Calling Sequence
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StandardError(S, A, ds_options)
StandardError[N](S, X, rv_options)
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Parameters
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S
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name; statistic
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A
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Array or Matrix data set; data sample
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N
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positive integer; sample size
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X
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algebraic; random variable or distribution
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ds_options
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(optional) equation(s) of the form option=value where option is one of ignore or weights; specify options for computing the standard error for a data set
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rv_options
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(optional) equation of the form numeric=value; specifies options for computing the standard error for a random variable
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Description
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In the data set case the sample size and all the relevant parameters (such as mean, standard deviation, etc.) will be estimated based on the specified data. All computations are performed under the assumption that the underlying sampling distribution is approximately normal.
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In the random variable case, N is the sample size.
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Computation
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By default, all computations involving random variables are performed symbolically (see option numeric below).
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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.
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Data Set Options
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ignore=truefalse -- This option controls how missing data is handled by the StandardError command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the StandardError command will return undefined. If ignore=true all missing items in A will be ignored. The default value is false.
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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 .
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Random Variable Options
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numeric=truefalse -- By default, the standard error is computed using exact arithmetic. To compute the standard error numerically, specify the numeric or numeric = true option.
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Compatibility
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The A parameter was updated in Maple 16.
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Examples
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Find the Standard Error of the mean on a sample drawn from the normal distribution.
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Consider the following Matrix data set.
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We compute the standard error of the interquartile range of each of the columns, and the standard error of the second moments of the columns with respect to different origins.
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