Understanding statistical distributions is key when working with data, as they help describe how values behave in a dataset. Different distributions are useful for modelling different types of real-world scenarios. This guide provides a brief overview of some of the most commonly used statistical distributions, along with their key properties and how to generate random samples from them using Python.
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Normal distribution
The normal distribution, also called the Gaussian distribution, is symmetric and bell-shaped, commonly used to model natural phenomena where values cluster around a central mean with a given spread.
Type: | Continuous |
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