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<!doctype html>
<html lang="en">
<head>
	<meta charset="UTF-8">
	<title>Vector - jStat Documentation</title>
	<link rel="stylesheet" href="assets/style.css" />
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</head>
<body>
	<div id="container">
    <header>
      <h1>jStat v1.9.3 Documentation</h1>
      <div id="gtoc">
        <p><a href="index.html">Index</a> | <a href="all.html">View on single page</a></p>
      </div>
      <hr />
    </header>
	<div id="toc"><h2>Table Of Contents</h2><ul><li><a href="#vector_Functionality">Vector Functionality</a><ul><li><a href="#sum">sum()</a></li><li><a href="#sumsqrd">sumsqrd()</a></li><li><a href="#sumsqerr">sumsqerr()</a></li><li><a href="#sumrow">sumrow()</a></li><li><a href="#product">product()</a></li><li><a href="#min">min()</a></li><li><a href="#max">max()</a></li><li><a href="#mean">mean()</a></li><li><a href="#meansqerr">meansqerr()</a></li><li><a href="#geomean">geomean()</a></li><li><a href="#median">median()</a></li><li><a href="#cumsum">cumsum()</a></li><li><a href="#cumprod">cumprod()</a></li><li><a href="#diff">diff()</a></li><li><a href="#rank">rank()</a></li><li><a href="#mode">mode()</a></li><li><a href="#range">range()</a></li><li><a href="#variance">variance()</a></li><li><a href="#pooledvariance">pooledvariance()</a></li><li><a href="#deviation">deviation()</a></li><li><a href="#stdev">stdev()</a></li><li><a href="#pooledstdev">pooledstdev()</a></li><li><a href="#meandev">meandev()</a></li><li><a href="#meddev">meddev()</a></li><li><a href="#skewness">skewness()</a></li><li><a href="#kurtosis">kurtosis()</a></li><li><a href="#coeffvar">coeffvar()</a></li><li><a href="#quartiles">quartiles()</a></li><li><a href="#quantiles">quantiles()</a></li><li><a href="#percentile">percentile()</a></li><li><a href="#percentileOfScore">percentileOfScore()</a></li><li><a href="#histogram">histogram()</a></li><li><a href="#covariance">covariance()</a></li><li><a href="#corrcoeff">corrcoeff()</a></li></ul></li></ul><hr /></div>
<h2 id="vector_Functionality">Vector Functionality</h2>

<h3 id="sum">sum()</h3>

<p><strong>sum( array )</strong></p>

<p>Returns the sum of the <code>array</code> vector.</p>

<pre><code>jStat.sum([1,2,3]) === 6</code></pre>

<p><strong>fn.sum( [bool][, callback] )</strong></p>

<p>Returns the sum of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).sum() === 15
jStat([[1,2],[3,4]]).sum() === [ 4, 6 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).sum(function( result ) {
    // result === 15
});</code></pre>

<p>If pass boolean true as first argument, then return sum of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).sum( true ) === 10</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).sum(true, function( result ) {
    // result === 10
});</code></pre>

<h3 id="sumsqrd">sumsqrd()</h3>

<p><strong>sumsqrd( array )</strong></p>

<p>Returns the sum squared of the <code>array</code> vector.</p>

<pre><code>jStat.sumsqrd([1,2,3]) === 14</code></pre>

<p><strong>fn.sumsqrd( [bool][, callback] )</strong></p>

<p>Returns the sum squared of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).sumsqrd() === 55
jStat([[1,2],[3,4]]).sumsqrd() === [ 10, 20 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).sumsqrd(function( result ) {
    // result === 55
});</code></pre>

<p>If pass boolean true as first argument, then return sum squared of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).sumsqrd( true ) === 650</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).sumsqrd(true,function( result ) {
    // result === 650
});</code></pre>

<h3 id="sumsqerr">sumsqerr()</h3>

<p><strong>sumsqerr( array )</strong></p>

<p>Returns the sum of squared errors of prediction of the <code>array</code> vector.</p>

<pre><code>jStat.sumsqerr([1,2,3]) === 2</code></pre>

<p><strong>fn.sumsqerr( [bool][, callback] )</strong></p>

<p>Returns the sum of squared errors of prediction of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).sumsqerr() === 10
jStat([[1,2],[3,4]]).sumsqerr() === [ 2, 2 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).sumsqerr(function( result ) {
    // result === 55
});</code></pre>

<p>If pass boolean true as first argument, then return sum of squared errors of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).sumsqerr( true ) === 0</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).sumsqerr(true,function( result ) {
    // result === 0
});</code></pre>

<h3 id="sumrow">sumrow()</h3>

<p><strong>sumrow( array )</strong></p>

<p>Returns the sum of the <code>array</code> vector in row-based order.</p>

<pre><code>jStat.sumrow([1,2,3]) === 6</code></pre>

<p><strong>fn.sumrow( [bool][, callback] )</strong></p>

<p>Returns the sum of a vector or matrix rows.</p>

<pre><code>jStat( 1, 5, 5 ).sumrow() === 15
jStat([[1,2],[3,4]]).sumrow() === [ 3, 7 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).sumrow(function( result ) {
    // result === 15
});</code></pre>

<p>If pass boolean true as first argument, then return sum of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).sumrow( true ) === 10</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).sumrow(true,function( result ) {
    // result === 10
});</code></pre>

<h3 id="product">product()</h3>

<p><strong>product( array )</strong></p>

<p>Returns the product of the <code>array</code> vector.</p>

<pre><code>jStat.product([1,2,3]) === 6</code></pre>

<p><strong>fn.product( [bool][, callback] )</strong></p>

<p>Returns the product of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).product() === 120
jStat([[1,2],[3,4]]).product() === [ 3, 8 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).product(function( result ) {
    // result === 120
});</code></pre>

<p>If pass boolean true as first argument, then return sumsqerr of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).product( true ) === 24</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).product(true,function( result ) {
    // result === 24
});</code></pre>

<h3 id="min">min()</h3>

<p><strong>min( array )</strong></p>

<p>Returns the minimum value of the <code>array</code> vector.</p>

<pre><code>jStat.min([1,2,3]) === 1</code></pre>

<p><strong>fn.min( [bool][, callback] )</strong></p>

<p>Returns the minimum value of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).min() === 1
jStat([[1,2],[3,4]]).min() === [ 1, 2 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).min(function( result ) {
    // result === 1
});</code></pre>

<p>If pass boolean true as first argument, then return minimum of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).min( true ) === 1</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).min(true,function( result ) {
    // result === 1
});</code></pre>

<h3 id="max">max()</h3>

<p><strong>max( array )</strong></p>

<p>Returns the maximum value of the <code>array</code> vector.</p>

<pre><code>jStat.max([1,2,3]) === 3</code></pre>

<p><strong>fn.max( [bool][, callback] )</strong></p>

<p>Returns the maximum value of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).max() === 5
jStat([[1,2],[3,4]]).max() === [ 3, 4 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).max(function( result ) {
    // result === 5
});</code></pre>

<p>If pass boolean true as first argument, then return maximum of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).max( true ) === 4</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).max(true,function( result ) {
    // result === 4
});</code></pre>

<h3 id="mean">mean()</h3>

<p><strong>mean( array )</strong></p>

<p>Returns the mean of the <code>array</code> vector.</p>

<pre><code>jStat.mean([1,2,3]) === 2</code></pre>

<p><strong>fn.max( [bool,][callback] )</strong></p>

<p>Returns the max of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).mean() === 3
jStat([[1,2],[3,4]]).mean() === [ 2, 3 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).mean(function( result ) {
    // result === 3
});</code></pre>

<p>If pass boolean true as first argument, then return mean of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).mean( true ) === 2.5</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).mean(true,function( result ) {
    // result === 2.5
});</code></pre>

<h3 id="meansqerr">meansqerr()</h3>

<p><strong>meansqerr( array )</strong></p>

<p>Returns the mean squared error of the <code>array</code> vector.</p>

<pre><code>jStat.meansqerr([1,2,3]) === 0.66666...</code></pre>

<p><strong>fn.meansqerr( [bool][, callback] )</strong></p>

<p>Returns the mean squared error of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).meansqerr() === 2
jStat([[1,2],[3,4]]).meansqerr() === [ 1, 1 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).meansqerr(function( result ) {
    // result === 2
});</code></pre>

<p>If pass boolean true as first argument, then return mean squared error of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).meansqerr( true ) === 0</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).meansqerr(true,function( result ) {
    // result === 0
});</code></pre>

<h3 id="geomean">geomean()</h3>

<p><strong>geomean( array )</strong></p>

<p>Returns the geometric mean of the <code>array</code> vector.</p>

<pre><code>jStat.geomean([4,1,1/32]) === 0.5</code></pre>

<p><strong>fn.geomean( [bool][, callback] )</strong></p>

<p>Returns the geometric mean of a vector or matrix columns.</p>

<pre><code>jStat([4,1,1\32]).geomean() === 0.5
jStat([[1,2],[3,4]]).geomean() === [ 1.732..., 2.828... ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat([4,1,1\32]).geomean(function( result ) {
    // result === 0.5
});</code></pre>

<p>If pass boolean true as first argument, then return geometric mean of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).geomean( true ) === 2.213...</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).geomean(true,function( result ) {
    // result === 2.213...
});</code></pre>

<h3 id="median">median()</h3>

<p><strong>median( array )</strong></p>

<p>Returns the median of the <code>array</code> vector.</p>

<pre><code>jStat.median([1,2,3]) === 2</code></pre>

<p><strong>fn.median( [bool][, callback] )</strong></p>

<p>Returns the median of a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).median() === 3
jStat([[1,2],[3,4]]).median() === [ 2, 3 ]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).median(function( result ) {
    // result === 3
});</code></pre>

<p>If pass boolean true as first argument, then return median of entire matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).median( true ) === 2.5</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).median(true,function( result ) {
    // result === 2.5
});</code></pre>

<h3 id="cumsum">cumsum()</h3>

<p><strong>cumsum( array )</strong></p>

<p>Returns an array of partial sums in the sequence.</p>

<pre><code>jStat.cumsum([1,2,3]) === [1,3,6]</code></pre>

<p><strong>fn.cumsum( [bool][, callback] )</strong></p>

<p>Returns an array of partial sums for a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).cumsum() === [1,3,6,10,15]
jStat([[1,2],[3,4]]).cumsum() === [[1,4],[2,6]]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).cumsum(function( result ) {
    // result === [1,3,6,10,15]
});</code></pre>

<p>If pass boolean true as first argument, then return cumulative sums of the matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).cumsum( true ) === [[1,3],[3,7]]</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).cumsum(true,function( result ) {
    // result === ...
});</code></pre>

<h3 id="cumprod">cumprod()</h3>

<p><strong>cumprod( array )</strong></p>

<p>Returns an array of partial products in the sequence.</p>

<pre><code>jStat.cumprod([2,3,4]) === [2,6,24]</code></pre>

<p><strong>fn.cumprod( [bool][, callback] )</strong></p>

<p>Returns an array of partial products for a vector or matrix columns.</p>

<pre><code>jStat( 1, 5, 5 ).cumprod() === [1,2,6,24,120]
jStat([[1,2],[3,4]]).cumprod() === [[1,3],[2,8]]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).cumprod(function( result ) {
    // result === [1,2,6,24,120]
});</code></pre>

<p>If pass boolean true as first argument, then return cumulative products of the matrix.</p>

<pre><code>jStat([[1,2],[3,4]]).cumprod( true ) === [[1,2],[3,12]]</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4]]).cumprod(true,function( result ) {
    // result === ...
});</code></pre>

<h3 id="diff">diff()</h3>

<p><strong>diff( array )</strong></p>

<p>Returns an array of the successive differences of the array.</p>

<pre><code>jStat.diff([1,2,2,3]) === [1,0,1]</code></pre>

<p><strong>fn.diff( [bool][, callback] )</strong></p>

<p>Returns an array of successive differences for a vector or matrix columns.</p>

<pre><code>jStat([1,2,2,3]).diff() === [1,0,1]
jStat([[1,2],[3,4],[1,4]]).diff() === [[2,-2],[2,0]]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat([[1,2],[3,4],[1,4]]).diff(function( result ) {
    // result === [[2,-2],[2,0]]
});</code></pre>

<p>If pass boolean true as first argument, then return successive difference for the whole matrix.</p>

<pre><code>jStat([[1,2],[3,4],[1,4]]).diff(true) === [0,2]</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,4],[1,4]]).diff(true,function( result ) {
    // result === [0,2]
});</code></pre>

<h3 id="rank">rank()</h3>

<p><strong>rank( array )</strong></p>

<p>Returns an array of the ranks of the array.</p>

<pre><code>jStat.rank([1, 2, 2, 3]) === [1, 2.5, 2.5, 4]</code></pre>

<p><strong>fn.rank( [bool][, callback] )</strong></p>

<p>Returns an array of ranks for a vector or matrix columns.</p>

<pre><code>jStat([1, 2, 2, 3]).rank() === [1, 2.5, 2.5, 4]
jStat([[1, 2], [3, 4], [1, 4]]).rank() === [[1.5, 3, 1.5], [1, 2.5, 2.5]]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat([[1, 2], [3, 4], [1, 4]]).rank(function( result ) {
    // result === [[1.5, 3, 1.5], [1, 2.5, 2.5]]
});</code></pre>

<p>If pass boolean true as first argument, then return rank for the whole matrix.</p>

<pre><code>jStat([[1, 2], [3, 4], [1, 4]]).rank(true) === [2, 5, 2, 5, 2, 5]</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1, 2], [3, 4], [1, 4]]).rank(true, function( result ) {
    // result === [2, 5, 2, 5, 2, 5]
});</code></pre>

<h3 id="mode">mode()</h3>

<p><strong>mode( array )</strong></p>

<p>Returns the mode of the <code>array</code> vector.
If there are multiple modes then <code>mode()</code> will return all of them.</p>

<pre><code>jStat.mode([1,2,2,3]) === 2
jStat.mode([1,2,3]) === [1,2,3]</code></pre>

<p><strong>fn.mode( [bool][, callback] )</strong></p>

<p>Returns the mode for a vector or matrix columns.</p>

<pre><code>jStat([1,2,2,3]).mode() === 2
jStat([[1,2],[3,4],[1,4]]).mode() === [1,4]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).mode(function( result ) {
    // result === false
});</code></pre>

<p>If pass boolean true as first argument, then the matrix will be treated as one
dimensional.</p>

<pre><code>jStat([[5,4],[5, 2], [5,2]]).mode( true ) === 5</code></pre>

<h3 id="range">range()</h3>

<p><strong>range( array )</strong></p>

<p>Returns the range of the <code>array</code> vector.</p>

<pre><code>jStat.range([1,2,3]) === 2</code></pre>

<p><strong>fn.range( [bool][, callback] )</strong></p>

<p>Returns the range for a vector or matrix columns.</p>

<pre><code>jStat([1,2,3]).range() === 2
jStat([[1,2],[3,4]]).range() === [2,2]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).range(function( result ) {
    // result === 4
});</code></pre>

<p>If pass boolean true as first argument, then return range of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).range( true ) === true</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).range(true,function( result ) {
    // result === 1
});</code></pre>

<h3 id="variance">variance()</h3>

<p><strong>variance( array[, flag] )</strong></p>

<p>Returns the variance of the <code>array</code> vector.
By default, the population variance is calculated.
Passing <code>true</code> to <code>flag</code> indicates to compute the sample variance instead.</p>

<pre><code>jStat.variance([1,2,3,4]) === 1.25
jStat.variance([1,2,3,4],true) === 1.66666...</code></pre>

<p><strong>fn.variance( [bool][, callback] )</strong></p>

<p>Returns the variance for a vector or matrix columns.</p>

<p><strong>Note:</strong> Cannot pass flag to indicate between population or sample for matrices.
There is a feature request for this on <a href="https://github.com/jstat/jstat/issues/51">Issue #51</a>.</p>

<pre><code>jStat([1,2,3,4]).variance() === 1.25
jStat([[1,2],[3,4]]).variance() === [1,1]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 5, 5 ).variance(function( result ) {
    // result === 2
});</code></pre>

<p>If pass boolean true as first argument, then return variance of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).variance( true ) === 0.140625</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).variance(true,function( result ) {
    // result === 0.140625
});</code></pre>

<h3 id="pooledvariance">pooledvariance()</h3>

<p><strong>pooledvariance( arrays )</strong></p>

<p>Returns the pooled (sample) variance of an array of vectors.
Assumes the population variance of the vectors are the same.</p>

<pre><code>jStat.pooledvariance([[1,2],[3,4]]) === 0.5</code></pre>

<h3 id="deviation">deviation()</h3>

<p><strong>deviation( array )</strong></p>

<p>Returns the deviation of the <code>array</code> vector.</p>

<pre><code>jStat.deviation([1,2,3,4]) === [-1.5, -0.5, 0.5, 1.5]</code></pre>

<p><strong>fn.deviation( [bool][, callback] )</strong></p>

<p>Returns the deviation for a vector or matrix columns.</p>

<pre><code>jStat([1,2,3,4]).deviation() === [-1.5, -0.5, 0.5, 1.5]
jStat([[1,2],[3,4]]).deviation() === [[-1,1],[-1,1]]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 4, 4 ).deviation(function( result ) {
    // result === [-1.5, -0.5, 0.5, 1.5]
});</code></pre>

<p>If pass boolean true as first argument, then return variance of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).deviation( true ) === [-0.5, 0.5, -1, 1]</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).deviation(true,function( result ) {
    // result === [-0.5, 0.5, -1, 1]
});</code></pre>

<h3 id="stdev">stdev()</h3>

<p><strong>stdev( array[, flag] )</strong></p>

<p>Returns the standard deviation of the <code>array</code> vector.
By default, the population standard deviation is returned.
Passing <code>true</code> to <code>flag</code> returns the sample standard deviation.</p>

<p>The &#39;sample&#39; standard deviation is also called the &#39;corrected standard deviation&#39;, and is an unbiased estimator of the population standard deviation.
The population standard deviation is also the &#39;uncorrected standard deviation&#39;, and is a biased but minimum-mean-squared-error estimator.</p>

<pre><code>jStat.stdev([1,2,3,4]) === 1.118...
jStat.stdev([1,2,3,4],true) === 1.290...</code></pre>

<p><strong>fn.stdev( [bool][, callback] )</strong></p>

<p>Returns the standard deviation for a vector or matrix columns.</p>

<p><strong>Note:</strong> Cannot pass <code>flag</code> to indicate between population or sample for matrices.
There is a feature request for this on <a href="https://github.com/jstat/jstat/issues/51">Issue #51</a>.</p>

<pre><code>jStat([1,2,3,4]).stdev() === 1.118...
jStat([1,2,3,4]).stdev(true) === 1.290...
jStat([[1,2],[3,4]]).stdev() === [1,1]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 4, 4 ).stdev(function( result ) {
    // result === 1.118...
});
jStat( 1, 4, 4 ).stdev(true,function( result ) {
    // result === 1.290...
});</code></pre>

<p>If pass boolean true as first argument, then return variance of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).stdev( true ) === 0.25</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).stdev(true,function( result ) {
    // result === 0.25
});</code></pre>

<h3 id="pooledstdev">pooledstdev()</h3>

<p><strong>pooledstdev( arrays )</strong></p>

<p>Returns the pooled (sample) standard deviation of an array of vectors.
Assumes the population standard deviation of the vectors are the same.</p>

<pre><code>jStat.pooledstdev([[1,2],[3,4]]) === 0.707...</code></pre>

<h3 id="meandev">meandev()</h3>

<p><strong>meandev( array )</strong></p>

<p>Returns the mean absolute deviation of the <code>array</code> vector.</p>

<pre><code>jStat.meandev([1,2,3,4]) === 1</code></pre>

<p><strong>fn.meandev( [bool][, callback] )</strong></p>

<p>Returns the mean absolute deviation for a vector or matrix columns.</p>

<pre><code>jStat([1,2,3,4]).meandev() === 1
jStat([[1,2],[3,4]]).meandev() === [1,1]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 4, 4 ).meandev(function( result ) {
    // result === 1
});</code></pre>

<p>If pass boolean true as first argument, then return mean absolute deviation of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).meandev( true ) === 0.25</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).meandev(true,function( result ) {
    // result === 0.25
});</code></pre>

<h3 id="meddev">meddev()</h3>

<p><strong>meddev( array )</strong></p>

<p>Returns the median absolute deviation of the <code>array</code> vector.</p>

<pre><code>jStat.meddev([1,2,3,4]) === 1</code></pre>

<p><strong>fn.meddev( [bool][, callback] )</strong></p>

<p>Returns the median absolute deviation for a vector or matrix columns.</p>

<pre><code>jStat([1,2,3,4]).meddev() === 1
jStat([[1,2],[3,4]]).meddev() === [1,1]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 4, 4 ).meddev(function( result ) {
    // result === 1
});</code></pre>

<p>If pass boolean true as first argument, then return median absolute deviation of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).meddev( true ) === 0.25</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).meddev(true,function( result ) {
    // result === 0.25
});</code></pre>

<h3 id="skewness">skewness()</h3>

<p><strong>skewness( array )</strong></p>

<p>Returns the skewness of the <code>array</code> vector (third standardized moment).</p>

<pre><code>jStat.skewness([1,2,2,3,5]) === 0.75003...</code></pre>

<h3 id="kurtosis">kurtosis()</h3>

<p><strong>kurtosis( array )</strong></p>

<p>Returns the excess kurtosis of the <code>array</code> vector (fourth standardized moment - 3).</p>

<pre><code>jStat.kurtosis([1,2,3,4]) === -0.63610...</code></pre>

<h3 id="coeffvar">coeffvar()</h3>

<p><strong>coeffvar( array )</strong></p>

<p>Returns the coefficient of variation of the <code>array</code> vector.</p>

<pre><code>jStat.coeffvar([1,2,3,4]) === 0.447...</code></pre>

<p><strong>fn.coeffvar( [bool][, callback] )</strong></p>

<p>Returns the coefficient of variation for a vector or matrix columns.</p>

<pre><code>jStat([1,2,3,4]).coeffvar() === 0.447...
jStat([[1,2],[3,4]]).coeffvar() === [0.5,0.333...]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat( 1, 4, 4 ).coeffvar(function( result ) {
    // result === 0.447...
});</code></pre>

<p>If pass boolean true as first argument, then return coefficient of variation of the matrix.</p>

<pre><code>jStat([[1,2],[3,5]]).coeffvar( true ) === 0.142...</code></pre>

<p>And the two can be combined.</p>

<pre><code>jStat([[1,2],[3,5]]).coeffvar(true,function( result ) {
    // result === 0.142...
});</code></pre>

<h3 id="quartiles">quartiles()</h3>

<p><strong>quartiles( array )</strong></p>

<p>Returns the quartiles of the <code>array</code> vector.</p>

<pre><code>jStat.quartiles( jStat.seq(1,100,100)) === [25,50,75]</code></pre>

<p><strong>fn.quartiles( [callback] )</strong></p>

<p>Returns the quartiles for a vector or matrix columns.</p>

<pre><code>jStat(1,100,100).quartiles() === [25,50,75]
jStat(1,100,100,function( x ) {
    return [x,x];
}).quartiles() === [[25,50,75],[25,50,75]]</code></pre>

<p>If callback is passed then will pass result as first argument.</p>

<pre><code>jStat(1,100,100).quartiles(function( result ) {
    // result === [25,50,75]
});</code></pre>

<h3 id="quantiles">quantiles()</h3>

<p><strong>quantiles( dataArray, quantilesArray[, alphap[, betap]] )</strong></p>

<p>Like quartiles, but calculate and return arbitrary quantiles of the <code>dataArray</code> vector
or matrix (column-by-column).</p>

<pre><code>jStat.quantiles([1, 2, 3, 4, 5, 6],
                [0.25, 0.5, 0.75]) === [1.9375, 3.5, 5.0625]</code></pre>

<p>Optional parameters alphap and betap govern the quantile estimation method.
For more details see the Wikipedia page on quantiles or scipy.stats.mstats.mquantiles
documentation.</p>

<h3 id="percentile">percentile()</h3>

<p><strong>percentile( dataArray, k, [exclusive] )</strong></p>

<p>Returns the k-th percentile of values in the <code>dataArray</code> range, where k is in the range 0..1, exclusive.
Passing true for the exclusive parameter excludes both endpoints of the range.</p>

<pre><code> jStat.percentile([1, 2, 3, 4], 0.3) === 1.9;
 jStat.percentile([1, 2, 3, 4], 0.3, true) === 1.5;</code></pre>

<h3 id="percentileOfScore">percentileOfScore()</h3>

<p><strong>percentileOfScore( dataArray, score[, kind] )</strong></p>

<p>The percentile rank of score in a given array. Returns the percentage
of all values in <code>dataArray</code> that are less than (if <code>kind == &#39;strict&#39;</code>) or
less or equal than (if <code>kind == &#39;weak&#39;</code>) score. Default is <code>&#39;weak&#39;</code>.</p>

<pre><code> jStat.percentileOfScore([1, 2, 3, 4, 5, 6], 3), 0.5, &#39;weak&#39;) === 0.5;</code></pre>

<h3 id="histogram">histogram()</h3>

<p><strong>histogram( dataArray[, numBins] )</strong></p>

<p>The histogram data defined as the number of <code>dataArray</code> elements found in
equally sized bins across the range of <code>dataArray</code>. Default number
of bins is 4.</p>

<pre><code> jStat.histogram([100, 101, 102, 230, 304, 305, 400], 3) === [3, 1, 3];</code></pre>

<h3 id="covariance">covariance()</h3>

<p><strong>covariance( array1, array2 )</strong></p>

<p>Returns the covariance of the <code>array1</code> and <code>array2</code> vectors.</p>

<pre><code>var seq = jStat.seq( 0, 10, 11 );
jStat.covariance( seq, seq ) === 11;</code></pre>

<h3 id="corrcoeff">corrcoeff()</h3>

<p><strong>corrcoeff( array1, array2 )</strong></p>

<p>Returns the population correlation coefficient of the <code>array1</code> and <code>array2</code> vectors (Pearson&#39;s Rho).</p>

<pre><code>var seq = jStat.seq( 0, 10, 11 );
jStat.corrcoeff( seq, seq ) === 1;</code></pre>

<p><strong>spearmancoeff( array1, array2 )</strong></p>

<p>Returns the rank correlation coefficient of the <code>array1</code> and <code>array2</code> vectors (Spearman&#39;s Rho).</p>

<pre><code>jStat.spearmancoeff([1, 2, 3, 4], [5, 6, 9, 7]) == 0.8;
jStat.spearmancoeff([1, 2, 2, 4], [5, 2, 5, 7]) == 0.5;</code></pre>
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