Statistical Analysis with Python

In this section, we will provide examples of the implementation of some of the most commonly used and useful statistical tools in Python, such as probability distributions, moment computations, loss functions (such as RMSE and MAE), OLS regression, and hypothesis testing.

We will delve deeper into each of these tools and provide detailed explanations and code examples to demonstrate their usage.

Bonus: Free statistics courses

There is a lot of free material online for people who want to dive deeper into statistics. Here is a selection from the Internet.

• Udacity’s “Intro to Statistics”

• Udacity’s “Intro to Descriptive Statistics”

• Udacity’s “Intro to Inferential Statistics”

• edX’s “Introduction to Probability - The Science of Uncertainty”

• Khan Academy’s videos on statistics and probability

• (Kaggle Kernel) Mathematics of Linear Regression by Nathan Lauga

Sources

• https://dataconomy.com/2015/02/introduction-to-bayes-theorem-with-python

• https://www.statisticshowto.datasciencecentral.com/discrete-variable/

• https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/binomial-theorem/binomial-distribution-formula/

• https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/descriptive-statistics/sample-variance/

• https://machinelearningmastery.com/a-gentle-introduction-to-the-bootstrap-method

• https://machinelearningmastery.com/k-fold-cross-validation/

• https://en.wikipedia.org/wiki/Poisson_distribution

• https://www.tutorialspoint.com/python/python_p_value.htm

• https://towardsdatascience.com/inferential-statistics-series-t-test-using-numpy-2718f8f9bf2f

• https://www.slideshare.net/dessybudiyanti/simple-linier-regression

• https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ

• https://en.wikipedia.org/wiki/Bias–variance_tradeoff