Code for the chapter exercises of Allen Downey's Think Stats 2nd Edition.

• an alternative approach to solving the book's exercises, utilising popular libraries from python's "scientific universe", namely numpy, scipy, statsmodels, pandas, matplotlib, seaborn, etc.

---

Introduction

First off, Think Stats is a great book ; short and sweet, with interesting real-life test studies that keep the reader interested & engaged. While there are paid versions, one can acquire a free version of the book. The author, Allen B. Downey, has continuously revised & updated the book's material and supporting code (with the aid of numerous contributors).

Per the book's description :

"Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding. "

The code contained in the book, follows an idiomatic object-oriented (OOP) approach ; the author's idea is to build a simple core library that defines discrete & continuous distributions, and then gradually extend that library to explain the various statistical concepts that the book covers.

By using python's scientific libraries to build and execute the book's exercises, one can get an introduction on computational statistics, while learning the mechanics and workflow of said libraries ; this way, when one decides to venture in more computational intensive fields (like bayesian analysis, machine learning), the use of the libraries feels less daunting & the transition (to a more functional programming style) less abrupt.

---

Dependencies - Libraries used

• Python 3.6 (code should also work for Python 2.7)
• Anaconda Python Data Science Distribution ; the easiest way to install & manage all scientific libraries used in this repo.

---

Reference Links

• Book's Main Site (along with links to all of the author's books) Think Stats

• Github Repository for the book's original code

• Allen B. Downey's personal blog Probably Overthinking It ; contains observations on computational statistics , mini projects , and much more.

• Computational Statistics | SciPy 2017 Tutorial - Hands-on Tutorial by Allen Downey from the Scipy 2017 Conference