The normal distribution and central limit theorem

When discussing the normal distribution, we refer to the bell-shaped, standard normal distribution, which is formally synonymous with the Gaussian distribution, named after Carl Friedrich Gauss, an 18th- and 19th-century mathematician and physicist who – among other things – contributed to the concepts of approximation, and, in 1795, invented the method of least squares and the normal distribution, which is commonly used in statistical modeling techniques, such as least squares regression [3]. The standard normal distribution, also referred to as a parametric distribution, is characterized by a symmetrical distribution with a probability of data point dispersion consistent around the mean – that is, the data appears near the mean more frequently than data farther away. Since the location data dispersed within this distribution follows the laws of probability, we can call this a standard normal probability distribution...