Gaussian Curves with Recharts

The normal distrubution (or, as it's more commonly known, the "bell curve" and, less commonly known, "Gaussian distribution") is one of the most widely known probability distributions. Its popularity is in part owed to the central limit theorem, which observes that with given a finite mean and variance, the average of many samples of a random variable will converge to a normal distribution as the number of samples increases. Put more succinctly, if you plot a bunch of observations of a random variable, chances are you will begin to see a bell curve.

The normal distribution is parameterized by two inputs, the mean and variance. Also called mu and sigma. You can provide these two parameters in the form below and see the resulting probability density function (PDF) and cumulative distribution function (CDF) of a normal distribution with those given parameters. The PDF was relatively straighforward to produce given mean and sigma as you can deterministicly return a point on the curve with those two parameters. The CDF was more complex as it required the approximation of the error function.