Understanding the normal distribution

The normal distribution is the core of inferential statistics. It is like a bell curve (also called a Gaussian curve). Most of the complex processes can be defined by the normal distribution.

Let's see what a normal distribution looks like. First, we will import the necessary packages. We are including RDatasets now, but will be using it later:

We first set the seed and then explore the normal function:

As per the warning given, we can also use fieldnames instead of names. It is recommended to use fieldnames only from the newer versions of Julia.

Here, we can see that the Normal function is in the Distributions package and has the features Univariate and Continuous. The constructor of the normal() function accepts two parameters:

• Mean (μ)

• Standard deviation (σ)

Let's instantiate a normal distribution. We will keep the mean (μ) as 1.0 and the standard deviation (σ) as 3.0:

We can check the mean and standard deviation that we have kept:

Using this normal...