In the Generating random data recipe, we generated random floating-point numbers following a uniform distribution between 0 and 1, but not including 1. However, in most cases where we require random data, we need to instead follow one of several different distributions. Roughly speaking, a distribution function is a function f(x) that describes the probability that a random variable has a value that is below x. In practical terms, the distribution describes the spread of the random data over a range. In particular, if we create a histogram of data that follows a particular distribution, then it should roughly resemble the graph of the distribution function. This is best seen by example.
One of the most common distributions is normal distribution, which appears frequently in statistics and forms the basis for many statistical methods that we will see in Chapter 6, Working with Data and Statistics. In this recipe, we will demonstrate...
