Linear Regression in Javascript

What kind of linear regression? For something simple like least squares, I'd just program it myself:

http://mathworld.wolfram.com/LeastSquaresFitting.html

The math is not too hard to follow there, give it a shot for an hour or so and let me know if it's too hard, I can try it.

EDIT:

Found someone that did it:

http://dracoblue.net/dev/linear-least-squares-in-javascript/159/

I found this great JavaScript library.

It's very simple, and seems to work perfectly.

I also can't recommend Math.JS enough.

The simplest solution I found for the question at hand can be found in the following post: http://trentrichardson.com/2010/04/06/compute-linear-regressions-in-javascript/

Note that in addition to the linear equation, it also returns the R2 score, which can be useful.

** EDIT **

Here is the actual code snippet:

function linearRegression(y,x){
        var lr = {};
        var n = y.length;
        var sum_x = 0;
        var sum_y = 0;
        var sum_xy = 0;
        var sum_xx = 0;
        var sum_yy = 0;

        for (var i = 0; i < y.length; i++) {

            sum_x += x[i];
            sum_y += y[i];
            sum_xy += (x[i]*y[i]);
            sum_xx += (x[i]*x[i]);
            sum_yy += (y[i]*y[i]);
        } 

        lr['slope'] = (n * sum_xy - sum_x * sum_y) / (n*sum_xx - sum_x * sum_x);
        lr['intercept'] = (sum_y - lr.slope * sum_x)/n;
        lr['r2'] = Math.pow((n*sum_xy - sum_x*sum_y)/Math.sqrt((n*sum_xx-sum_x*sum_x)*(n*sum_yy-sum_y*sum_y)),2);

        return lr;
}

To use this you just need to pass it two arrays, known_y's and known_x's, so this is what you might pass:

var known_y = [1, 2, 3, 4];
var known_x = [5.2, 5.7, 5.0, 4.2];

var lr = linearRegression(known_y, known_x);
// now you have:
// lr.slope
// lr.intercept
// lr.r2

Simple linear regression with measures of variation ( Total sum of squares = Regression sum of squares + Error sum of squares ), Standard error of estimate SEE (Residual standard error), and coefficients of determination R2 and correlation R.

const regress = (x, y) => {
    const n = y.length;
    let sx = 0;
    let sy = 0;
    let sxy = 0;
    let sxx = 0;
    let syy = 0;
    for (let i = 0; i < n; i++) {
        sx += x[i];
        sy += y[i];
        sxy += x[i] * y[i];
        sxx += x[i] * x[i];
        syy += y[i] * y[i];
    }
    const mx = sx / n;
    const my = sy / n;
    const yy = n * syy - sy * sy;
    const xx = n * sxx - sx * sx;
    const xy = n * sxy - sx * sy;
    const slope = xy / xx;
    const intercept = my - slope * mx;
    const r = xy / Math.sqrt(xx * yy);
    const r2 = Math.pow(r,2);
    let sst = 0;
    for (let i = 0; i < n; i++) {
       sst += Math.pow((y[i] - my), 2);
    }
    const sse = sst - r2 * sst;
    const see = Math.sqrt(sse / (n - 2));
    const ssr = sst - sse;
    return {slope, intercept, r, r2, sse, ssr, sst, sy, sx, see};
}
regress([1, 2, 3, 4, 5], [1, 2, 3, 4, 3]);