Weibull distribution standard deviation.
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> [Weibull][weibull-distribution] distribution [standard deviation][standard-deviation].
The [standard deviation][standard-deviation] for a [Weibull][weibull-distribution] random variable is
where λ > 0 is the [shape parameter][shape], k > 0 is the [scale parameter][scale], and Γ denotes the gamma function.
``bash`
npm install @stdlib/stats-base-dists-weibull-stdev
`javascript`
var stdev = require( '@stdlib/stats-base-dists-weibull-stdev' );
#### stdev( k, lambda )
Returns the [standard deviation][standard-deviation] of a [Weibull][weibull-distribution] distribution with parameters k (shape parameter) and lambda (scale parameter).
`javascript
var v = stdev( 1.0, 1.0 );
// returns 1.0
v = stdev( 4.0, 12.0 );
// returns ~3.051
v = stdev( 8.0, 2.0 );
// returns ~0.279
`
If provided NaN as any argument, the function returns NaN.
`javascript
var v = stdev( NaN, 2.0 );
// returns NaN
v = stdev( 2.0, NaN );
// returns NaN
`
If provided k <= 0, the function returns NaN.
`javascript
var v = stdev( 0.0, 1.0 );
// returns NaN
v = stdev( -1.0, 1.0 );
// returns NaN
`
If provided lambda <= 0, the function returns NaN.
`javascript
var v = stdev( 1.0, 0.0 );
// returns NaN
v = stdev( 1.0, -1.0 );
// returns NaN
`
`javascript
var uniform = require( '@stdlib/random-array-uniform' );
var logEachMap = require( '@stdlib/console-log-each-map' );
var EPS = require( '@stdlib/constants-float64-eps' );
var stdev = require( '@stdlib/stats-base-dists-weibull-stdev' );
var opts = {
'dtype': 'float64'
};
var lambda = uniform( 10, EPS, 10.0, opts );
var k = uniform( 10, EPS, 10.0, opts );
logEachMap( 'k: %0.4f, λ: %0.4f, SD(X;k,λ): %0.4f', k, lambda, stdev );
`
*
`c`
#include "stdlib/stats/base/dists/weibull/stdev.h"
#### stdlib_base_dists_weibull_stdev( k, lambda )
Returns the standard deviation of a Weibull distribution.
`c`
double out = stdlib_base_dists_weibull_stdev( 4.0, 12.0 );
// returns ~3.051
The function accepts the following arguments:
- k: [in] double shape parameter.[in] double
- lambda: scale parameter.
`c`
double stdlib_base_dists_weibull_stdev( const double k, const double lambda );
`c
#include "stdlib/stats/base/dists/weibull/stdev.h"
#include
#include
static double random_uniform( const double min, const double max ) {
double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
return min + ( v*(max-min) );
}
int main( void ) {
double lambda;
double k;
double y;
int i;
for ( i = 0; i < 25; i++ ) {
k = random_uniform( 0.0, 10.0 );
lambda = random_uniform( 0.0, 10.0 );
y = stdlib_base_dists_weibull_stdev( k, lambda );
printf( "k: %lf, λ: %lf, SD(X;k,λ): %lf\n", k, lambda, y );
}
}
`
*
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
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---
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[weibull-distribution]: https://en.wikipedia.org/wiki/Weibull_distribution
[standard-deviation]: https://en.wikipedia.org/wiki/Standard_deviation
[shape]: https://en.wikipedia.org/wiki/Shape_parameter
[scale]: https://en.wikipedia.org/wiki/Scale_parameter