Think Stats Exploratory Data Analysis in Python Version 2.0.35 Think Stats Exploratory Data Analysis in Python Version 2.0.35 Allen B. Downey Green Tea Press Needham, Massachusetts Copyright c 2014 Allen B. Downey. Green Tea Press 9 Washburn Ave Needham MA 02492 Permission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, which is available at http://creativecommons.org/licenses/ by-nc-sa/4.0/. The original form of this book is LATEX source code. Compiling this code has the effect of generating a device-independent representation of a textbook, which can be converted to other formats and printed. The LATEX source for this book is available from http://thinkstats2.com. Preface This book is an introduction to the practical tools of exploratory data analysis. The organization of the book follows the process I use when I start working with a dataset: Importing and cleaning: Whatever format the data is in, it usually takes some time and effort to read the data, clean and transform it, and check that everything made it through the translation process intact. Single variable explorations: I usually start by examining one variable at a time, finding out what the variables mean, looking at distributions of the values, and choosing appropriate summary statistics. Pair-wise explorations: To identify possible relationships between variables, I look at tables and scatter plots, and compute correlations and linear fits. Multivariate analysis: If there are apparent relationships between variables, I use multiple regression to add control variables and investigate more complex relationships. Estimation and hypothesis testing: When reporting statistical results, it is important to answer three questions: How big is the effect? How much variability should we expect if we run the same measurement again? Is it possible that the apparent effect is due to

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