Probability distributions are the foundation of statistical models. Natural processes generate data and empirical shape of data can be approximated by mathematical functions.

Some of well-known continuous probability functions are: (i) Normal distribution, (ii) Uniform distribution, (iii) Cauchy distribution, (iv) t distribution, (v) F distribution, (vi) Chi-Square distribution, (vii) Exponential distribution, (viii) Gamma distribution.

**EDA** can be used to understand the data intuition, understand the shape of it, and try to connect your understanding of the process that generated the data to the data itself.

**Fitting a model** – Fitting a model means estimating the parameters of the model using the observed data. It involves optimization methods and algorithms, such as *maximum likelihood estimation*, to help get the parameters.

**Overfitting** – Overfitting is the term used to mean that you used a dataset to estimate the parameters of your model, but your model is not that good at capturing reality beyond your sampled data.

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