# Compute Linear Regressions in Javascript Its very seldom, but now and then you have to go back to those old math equations from college and compute some nice graphs, or solve a few complex formulas. Linear Regressions aren't completely foreign territory, they are used more often than you realize. I've put together a nice function which computes slope, intercept, and r squared in javascript (its not dependent on a framework). If you're curious of what the equations for these functions look like you can peak at their corresponding Excel functions here:

• Intercept resembles Excel's intercept(y,x) function
• Slope resembles Excel's linest(y,x) function
• r2 resembles Excel's rsq(y,x) function

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 &lt; 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 = linearRregression(known_y, known_x);
// now you have:
// lr.slope
// lr.intercept
// lr.r2
```

Simple as that! Hope this helps someone out!