Knowing the underlying (probability) distribution of your data has many modeling advantages. The easiest manner to determine the underlying distribution is by visually inspecting the random variable(s) using a histogram. With the candidate distribution, various plots can be created such as the Probability Distribution Function plot (PDF/CDF), and the QQ plot. However, to determine the exact distribution parameters (e.g., loc, scale), it is essential to use quantitative methods. In this blog, I will describe why it is important to determine the underlying probability distribution for your data set. What the differences are between parametric and non-parametric distributions. How to determine the best fit using a quantitative approach and how to confirm it using visual inspections. Analyses are performed using the distfit library, and a notebook is accompanied for easy access and experimenting.
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