Think Stats

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This updated new edition shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you'll learn the entire process of exploratory data analysis--from collecting data and generating statistics to identifying patterns and testing hypotheses. This edition has been fully moved into Jupyter notebooks, so you can now read the text, run the code, and work on exercises all in one place. The notebooks are also designed to work on Google Colab, allowing you to get started without installing anything. And the updated code examples and exercises make use of the empiricaldist library to represent distributions. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that's cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data