Linear Regression in simple structure. Accepts a data object with x + y attributes. Returns a y = mx + b style line with an r squared value. Great for D3

linearRegression.js

const linearRegression = (data, y_attr, x_attr) => {

  //separate data into x and y sets

  let y_data = [];
  let x_data = [];

  for (let i = 0; i < data.length; i++) {
    y_data.push(data[i][y_attr]);
    x_data.push(data[i][x_attr]);
  }

  // set regression variables

  let lr = {};
  const n = y_data.length;
  let sum_x = 0;
  let sum_y = 0;
  let sum_xy = 0;
  let sum_xx = 0;
  let sum_yy = 0;

  for (let i = 0; i < n; i++) {
    sum_x += x_data[i];
    sum_y += y_data[i];
    sum_xy += (x_data[i] * y_data[i]);
    sum_xx += (x_data[i] * x_data[i]);
    sum_yy += (y_data[i] * y_data[i]);
  }

  // port variables into lr objects

  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;
}

const data = [ { x: 1, y: 1 },
               { x: 2, y: 2 },
               { x: 3, y: 3 } ];

console.log(linearRegression(data, 'y', 'x'));

// returns { slope: 1, intercept: 0, r2: 1 } YAY!

// adaption from script supplied in Trent Richardson's April 2010 post

If you're looking for a simple solution like this, I recommend Simple Statistics

Here is a CodePen with a simple D3 v4 implementation of a Line chart