# incrmeanvar > Compute an [arithmetic mean][arithmetic-mean] and an [unbiased sample variance][sample-variance] incrementally.
The [arithmetic mean][arithmetic-mean] is defined as
Equation for the arithmetic mean.
and the [unbiased sample variance][sample-variance] is defined as
Equation for the unbiased sample variance.
## Usage ```javascript var incrmeanvar = require( '@stdlib/stats/incr/meanvar' ); ``` #### incrmeanvar( \[out] ) Returns an accumulator `function` which incrementally computes an [arithmetic mean][arithmetic-mean] and [unbiased sample variance][sample-variance]. ```javascript var accumulator = incrmeanvar(); ``` By default, the returned accumulator `function` returns the accumulated values as a two-element `array`. To avoid unnecessary memory allocation, the function supports providing an output (destination) object. ```javascript var Float64Array = require( '@stdlib/array/float64' ); var accumulator = incrmeanvar( new Float64Array( 2 ) ); ``` #### accumulator( \[x] ) If provided an input value `x`, the accumulator function returns updated accumulated values. If not provided an input value `x`, the accumulator function returns the current accumulated values. ```javascript var accumulator = incrmeanvar(); var mv = accumulator(); // returns null mv = accumulator( 2.0 ); // returns [ 2.0, 0.0 ] mv = accumulator( 1.0 ); // returns [ 1.5, 0.5 ] mv = accumulator( 3.0 ); // returns [ 2.0, 1.0 ] mv = accumulator( -7.0 ); // returns [ -0.25, ~20.92 ] mv = accumulator( -5.0 ); // returns [ -1.2, 20.2 ] mv = accumulator(); // returns [ -1.2, 20.2 ] ```
## Notes - Input values are **not** type checked. If provided `NaN`, the accumulated values are equal to `NaN` for **all** future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
## Examples ```javascript var randu = require( '@stdlib/random/base/randu' ); var Float64Array = require( '@stdlib/array/float64' ); var ArrayBuffer = require( '@stdlib/array/buffer' ); var incrmeanvar = require( '@stdlib/stats/incr/meanvar' ); var offset; var acc; var buf; var out; var mv; var N; var v; var i; var j; // Define the number of accumulators: N = 5; // Create an array buffer for storing accumulator output: buf = new ArrayBuffer( N*2*8 ); // 8 bytes per element // Initialize accumulators: acc = []; for ( i = 0; i < N; i++ ) { // Compute the byte offset: offset = i * 2 * 8; // stride=2, bytes_per_element=8 // Create a new view for storing accumulated values: out = new Float64Array( buf, offset, 2 ); // Initialize an accumulator which will write results to the view: acc.push( incrmeanvar( out ) ); } // Simulate data and update the sample means and variances... for ( i = 0; i < 100; i++ ) { for ( j = 0; j < N; j++ ) { v = randu() * 100.0 * (j+1); acc[ j ]( v ); } } // Print the final results: console.log( 'Mean\tVariance' ); for ( i = 0; i < N; i++ ) { mv = acc[ i ](); console.log( '%d\t%d', mv[ 0 ].toFixed( 3 ), mv[ 1 ].toFixed( 3 ) ); } ```
[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean [sample-variance]: https://en.wikipedia.org/wiki/Variance