We can derive some fundamental distributions with the help of Normal distribution. Enlist these fundamental distributions and explain each one of them in detail.

Normal distribution, also known as Gaussian distribution or Bell curve, is one of the most widely used distributions in statistics and probability theory. The Normal distribution is characterized by its mean and standard deviation, and it is a continuous, symmetric, and unimodal distribution. It has several important properties that make it an indispensable tool in statistical analysis and modeling.

One of the main advantages of Normal distribution is its versatility in modeling real-world data. Many physical, biological, and economic variables follow a Normal distribution, such as the height of individuals, blood pressure, and stock returns. This allows us to make probabilistic predictions about these variables based on their mean and standard deviation.

One of the most important distributions derived from the Normal distribution is the standard Normal distribution, which is a Normal distribution with a mean of zero and a standard deviation of one. The standard Normal distribution is widely used as a benchmark to compare other Normal distributions, and it has several useful properties. For example, we can use the standard Normal distribution to find the probability of a random variable being less than or equal to a given value.

Another important distribution derived from the Normal distribution is the t-distribution. The t-distribution is used to estimate the mean of a population when the sample size is small or when the population variance is unknown. Unlike the Normal distribution, the t-distribution has heavier tails, meaning that it allows for more extreme values. The _____ _____ _______ _____ _____ ___ _________ ________ ____.
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