Q-Q Plots

<!DOCTYPE html>
<meta charset="utf-8">
<title>Q-Q Plots</title>
<style>

body {
  font: 10px sans-serif;
  width: 960px;
  height: 310px;
}

.qq .box,
.qq .tick line,
.qq .quantile,
.qq .diagonal {
  stroke: #aaa;
  fill: none;
}

.qq .quantile {
  stroke: #000;
}

.qq g + g .y.tick {
  display: none;
}

</style>
<body>
<script src="//d3js.org/d3.v3.min.js"></script>
<script src="d3.qq.min.js"></script>
<script>

var width = 270,
    height = 270,
    margin = {top: 20, right: 10, bottom: 20, left: 35},
    n = 10000; // number of samples to generate

var chart = d3.qq()
    .width(width)
    .height(height)
    .domain([-.1, 1.1])
    .tickFormat(function(d) { return ~~(d * 100); });

var vis = d3.select("body").append("svg")
    .attr("width", (width + margin.right + margin.left) * 3)
    .attr("height", height + margin.top + margin.bottom)
  .append("g")
    .attr("transform", "translate(" + margin.left + "," + margin.top + ")");

d3.json("turkers.json", function(error, turkers) {
  if (error) throw error;

var tm = mean(turkers),
      td = Math.sqrt(variance(turkers)),
      dd = [
        [0.10306430789206111, 0.0036139086950272735, 0.30498647327844536],
        [0.5924252668569606, 0.0462763685758622, 0.4340870312025223],
        [0.9847627827855167, 2.352350767874714e-4, 0.2609264955190324]
      ];

var g = vis.selectAll("g")
      .data([{
        x: d3.range(n).map(Math.random),
        y: turkers,
        label: "Uniform Distribution"
      }, {
        x: d3.range(n).map(normal1(tm, td)),
        y: turkers,
        label: "Gaussian (Normal) Distribution"
      }, {
        x: d3.range(n).map(normal3(dd)),
        y: turkers,
        label: "Mixture of 3 Gaussians"
      }])
    .enter().append("g")
      .attr("class", "qq")
      .attr("transform", function(d, i) { return "translate(" + (width + margin.right + margin.left) * i + ")"; });

g.append("rect")
      .attr("class", "box")
      .attr("width", width)
      .attr("height", height);

g.call(chart);

g.append("text")
      .attr("dy", "1.3em")
      .attr("dx", ".6em")
      .text(function(d) { return d.label; });

chart.duration(1000);

window.transition = function() {
    g.datum(randomize).call(chart);
  };
});

function randomize(d) {
  d.y = d3.range(n).map(Math.random);
  return d;
}

// Sample from a normal distribution with mean 0, stddev 1.
function normal() {
  var x = 0, y = 0, rds, c;
  do {
    x = Math.random() * 2 - 1;
    y = Math.random() * 2 - 1;
    rds = x * x + y * y;
  } while (rds == 0 || rds > 1);
  c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform
  return x * c; // throw away extra sample y * c
}

// Simple 1D Gaussian (normal) distribution
function normal1(mean, deviation) {
  return function() {
    return mean + deviation * normal();
  };
}

// Gaussian Mixture Model (k=3) fit using E-M algorithm
function normal3(dd) {
  return function() {
    var r = Math.random(),
        i = r < dd[0][2] ? 0 : r < dd[0][2] + dd[1][2] ? 1 : 2,
        d = dd[i];
    return d[0] + Math.sqrt(d[1]) * normal();
  }
}

// Welford's algorithm.
function mean(x) {
  var n = x.length;
  if (n === 0) return NaN;
  var m = 0,
      i = -1;
  while (++i < n) m += (x[i] - m) / (i + 1);
  return m;
}

// Unbiased estimate of a sample's variance.
// Also known as the sample variance, where the denominator is n - 1.
function variance(x) {
  var n = x.length;
  if (n < 1) return NaN;
  if (n === 1) return 0;
  var m = mean(x),
      i = -1,
      s = 0;
  while (++i < n) {
    var v = x[i] - m;
    s += v * v;
  }
  return s / (n - 1);
}

</script>

https://d3js.org/d3.v3.min.js