![]() Some exercises involve experiments to test statistical behavior. When you write programs, you express your understanding in code while you are debugging the program, you are also correcting your understanding. Each chapter includes exercises readers can do to develop and solidify their learning. In general, Python code is more readable also, because it is executable, readers can download it, run it, and modify it. Preface I present most ideas using Python code, rather than mathematical notation. This book takes a computational approach, which has several advantages over mathematical approaches:Ħ vi Chapter 0. Then if an apparent effect holds up to scrutiny, visualization is an effective way to communicate results. 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 chance? Visualization: During exploration, visualization is an important tool for finding possible relationships and effects. Multivariate analysis: If there are apparent relationships between variables, I use multiple regression to add control variables and investigate more complex relationships. Pair-wise explorations: To identify possible relationships between variables, I look at tables and scatter plots, and compute correlations and linear fits. 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. 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. ![]() The L A TEX source for this book is available fromĥ Preface This book is an introduction to the practical tools of exploratory data analysis. 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 original form of this book is L A TEX source code. Green Tea Press 9 Washburn Ave Needham MA 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 by-nc-sa/4.0/. Downey Green Tea Press Needham, MassachusettsĤ Copyright 2014 Allen B. 1 Think Stats Exploratory Data Analysis in Python Versionģ Think Stats Exploratory Data Analysis in Python Version Allen B.
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