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| john tukey exploratory data analysis: Exploratory Data Analysis John Wilder Tukey, 1970 |
| john tukey exploratory data analysis: Exploratory Data Analysis John Wilder Tukey, 1977 This book serves as an introductory text for exploratory data analysis. It exposes readers and users to a variety of techniques for looking more effectively at data. The emphasis is on general techniques, rather than specific problems. |
| john tukey exploratory data analysis: Understanding Robust and Exploratory Data Analysis David C. Hoaglin, Frederick Mosteller, John W. Tukey, 2000-06-02 Originally published in hardcover in 1982, this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use exploratory data analysis and robust and resistant methods in statistical practice. |
| john tukey exploratory data analysis: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| john tukey exploratory data analysis: The Concise Encyclopedia of Statistics Yadolah Dodge, 2008-04-15 The Concise Encyclopedia of Statistics presents the essential information about statistical tests, concepts, and analytical methods in language that is accessible to practitioners and students of the vast community using statistics in medicine, engineering, physical science, life science, social science, and business/economics. The reference is alphabetically arranged to provide quick access to the fundamental tools of statistical methodology and biographies of famous statisticians. The more than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings. All entries include cross-references as well as the key citations. The back matter includes a timeline of statistical inventions. This reference will be an enduring resource for locating convenient overviews about this essential field of study. |
| john tukey exploratory data analysis: The Practice of Data Analysis David R. Brillinger, Luisa T. Fernholz, Stephan Morgenthaler, 2014-07-14 This collection of essays brings together many of the world's most distinguished statisticians to discuss a wide array of the most important recent developments in data analysis. The book honors John W. Tukey, one of the most influential statisticians of the twentieth century, on the occasion of his eightieth birthday. Contributors, some of them Tukey's former students, use his general theoretical work and his specific contributions to Exploratory Data Analysis as the point of departure for their papers. They cover topics from pure data analysis, such as gaussianizing transformations and regression estimates, and from applied subjects, such as the best way to rank the abilities of chess players or to estimate the abundance of birds in a particular area. Tukey may be best known for coining the common computer term bit, for binary digit, but his broader work has revolutionized the way statisticians think about and analyze sets of data. In a personal interview that opens the book, he reviews these extraordinary contributions and his life with characteristic modesty, humor, and intelligence. The book will be valuable both to researchers and students interested in current theoretical and practical data analysis and as a testament to Tukey's lasting influence. The essays are by Dhammika Amaratunga, David Andrews, David Brillinger, Christopher Field, Leo Goodman, Frank Hampel, John Hartigan, Peter Huber, Mia Hubert, Clifford Hurvich, Karen Kafadar, Colin Mallows, Stephan Morgenthaler, Frederick Mosteller, Ha Nguyen, Elvezio Ronchetti, Peter Rousseeuw, Allan Seheult, Paul Velleman, Maria-Pia Victoria-Feser, and Alessandro Villa. Originally published in 1998. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. |
| john tukey exploratory data analysis: Fundamentals of Exploratory Analysis of Variance David C. Hoaglin, Frederick Mosteller, John W. Tukey, 2009-09-25 The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters include exercises and the appendices give selected percentage points of the Gaussian, t, F chi-squared and studentized range distributions. |
| john tukey exploratory data analysis: Applications, Basics, and Computing of Exploratory Data Analysis Paul F. Velleman, David Caster Hoaglin, 1981 Stem-and-left displays; Letter-value displays; Boxplots; x-y plotting; Resistant line; Smoothing data; Coded tables; Median polish; Rootograms; Computer graphics; Utility programs; Programming conventions; Minitab implementation; Appendices; Index. |
| john tukey exploratory data analysis: Exploratory Data Analysis with MATLAB Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka, 2017-08-07 Praise for the Second Edition: The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB. —Adolfo Alvarez Pinto, International Statistical Review Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data |
| john tukey exploratory data analysis: Exploring Data Tables, Trends, and Shapes David C. Hoaglin, Frederick Mosteller, John W. Tukey, 2011-09-28 WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Exploring Data Tables, Trends, and Shapes (EDTTS) was written as a companion volume to the same editors' book, Understanding Robust and Exploratory Data Analysis (UREDA). Whereas UREDA is a collection of exploratory and resistant methods of estimation and display, EDTTS goes a step further, describing multivariate and more complicated techniques . . . I feel that the authors have made a very significant contribution in the area of multivariate nonparametric methods. This book [is] a valuable source of reference to researchers in the area. —Technometrics This edited volume . . . provides an important theoretical and philosophical extension to the currently popular statistical area of Exploratory Data Analysis, which seeks to reveal structure, or simple descriptions, in data . . . It is . . . an important reference volume which any statistical library should consider seriously. —The Statistician This newly available and affordably priced paperback version of Exploring Data Tables, Trends, and Shapes presents major advances in exploratory data analysis and robust regression methods and explains the techniques, relating them to classical methods. The book addresses the role of exploratory and robust techniques in the overall data-analytic enterprise, and it also presents new methods such as fitting by organized comparisons using the square combining table and identifying extreme cells in a sizable contingency table with probabilistic and exploratory approaches. The book features a chapter on using robust regression in less technical language than available elsewhere. Conceptual support for each technique is also provided. |
| john tukey exploratory data analysis: Hands-On Exploratory Data Analysis with Python Suresh Kumar Mukhiya, Usman Ahmed, 2020-03-27 Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book. |
| john tukey exploratory data analysis: Data Analysis and Regression Frederick Mosteller, John Wilder Tukey, 2019-04-18 This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearson.com/statistics-classics-series for a complete list of titles. Two mainstreams intermingle in this treatment of practical statistics: (a) a sequence of philosophical attitudes the student needs for effective data analysis, and (b) a flow of useful and adaptable techniques that make it possible to put these attitudes to work. 0134995333 / 9780134995335 DATA ANALYSIS AND REGRESSION: A SECOND COURSE IN STATISTICS (CLASSIC VERSION), 1/e |
| john tukey exploratory data analysis: Exploratory Data Mining and Data Cleaning Tamraparni Dasu, Theodore Johnson, 2003-08-01 Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. |
| john tukey exploratory data analysis: The Collected Works of John W. Tukey L.V. Jones, 1987-05-15 This volume of eleven articles compiles important papers by Tukey that examine the intriguing problems inherent in the area of multiple comparisons and provide a useful framework for thinking about them. Each volume in the set is indexed and contains a bibliography. |
| john tukey exploratory data analysis: Info We Trust RJ Andrews, 2019-01-03 How do we create new ways of looking at the world? Join award-winning data storyteller RJ Andrews as he pushes beyond the usual how-to, and takes you on an adventure into the rich art of informing. Creating Info We Trust is a craft that puts the world into forms that are strong and true. It begins with maps, diagrams, and charts — but must push further than dry defaults to be truly effective. How do we attract attention? How can we offer audiences valuable experiences worth their time? How can we help people access complexity? Dark and mysterious, but full of potential, data is the raw material from which new understanding can emerge. Become a hero of the information age as you learn how to dip into the chaos of data and emerge with new understanding that can entertain, improve, and inspire. Whether you call the craft data storytelling, data visualization, data journalism, dashboard design, or infographic creation — what matters is that you are courageously confronting the chaos of it all in order to improve how people see the world. Info We Trust is written for everyone who straddles the domains of data and people: data visualization professionals, analysts, and all who are enthusiastic for seeing the world in new ways. This book draws from the entirety of human experience, quantitative and poetic. It teaches advanced techniques, such as visual metaphor and data transformations, in order to create more human presentations of data. It also shows how we can learn from print advertising, engineering, museum curation, and mythology archetypes. This human-centered approach works with machines to design information for people. Advance your understanding beyond by learning from a broad tradition of putting things “in formation” to create new and wonderful ways of opening our eyes to the world. Info We Trust takes a thoroughly original point of attack on the art of informing. It builds on decades of best practices and adds the creative enthusiasm of a world-class data storyteller. Info We Trust is lavishly illustrated with hundreds of original compositions designed to illuminate the craft, delight the reader, and inspire a generation of data storytellers. |
| john tukey exploratory data analysis: Modern Data Analysis Robert L. Launer, Andrew F. Siegel, 2014-05-12 Modern Data Analysis contains the proceedings of a Workshop on Modern Data Analysis held in Raleigh, North Carolina, on June 2-4, 1980 under the auspices of the United States Army Research Office. The papers review theories and methods of data analysis and cover topics ranging from single and multiple quantile-quantile (Q-Q) plotting procedures to biplot display and pencil-and-paper exploratory data analysis methods. Projection pursuit methods for data analysis are also discussed. Comprised of nine chapters, this book begins with an introduction to styles of data analysis techniques, followed by an analysis of single and multiple Q-Q plotting procedures. Problems involving extreme-value data and the behavior of sample averages are considered. Subsequent chapters deal with the use of smelting in guiding re-expression; geometric data analysis; and influence functions and regression diagnostics. The final chapter examines the use and interpretation of robust analysis of variance for the general non-full-rank linear model. The procedures are described in terms of their mathematical structure, which leads to efficient computational algorithms. This monograph should be of interest to mathematicians and statisticians. |
| john tukey exploratory data analysis: Encyclopedia of Mathematical Geosciences B. S. Daya Sagar, Qiuming Cheng, Jennifer McKinley, Frits Agterberg, 2023-07-13 The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive. |
| john tukey exploratory data analysis: Convergence and Uniformity in Topology John W. Tukey, 2016-03-02 A classic treatment of convergence and uniformity in topology from the acclaimed Annals of Mathematics Studies series Princeton University Press is proud to have published the Annals of Mathematics Studies since 1940. One of the oldest and most respected series in science publishing, it has included many of the most important and influential mathematical works of the twentieth century. The series continues this tradition as Princeton University Press publishes the major works of the twenty-first century. To mark the continued success of the series, all books are available in paperback and as ebooks. |
| john tukey exploratory data analysis: Python Data Science Essentials Alberto Boschetti, Luca Massaron, 2016-10-28 Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets. |
| john tukey exploratory data analysis: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| john tukey exploratory data analysis: Understanding Data Erickson , B, Nosanchuk, T, 1992-09-01 For statistics to be used by sociologists, and especially by students of sociology, they must first be easy to understand and use. Accordingly this book is aimed at that legion of professional sociologists and students who have always feared numbers; it employs much visual display, for example, as an easy way into the data. Also, the book is written in a relaxed and enthusiastic way that reassures apprehensive students without watering down what they must be taught. Classical statistics were developed to meet the requirements of the natural sciences; as such they reflect the more deductive nature of hypothesis development in these sciences. However, they have offered the sociologists little in the way of techniques for exploring messy data in the context of incomplete theories. This book attempts to remedy those weaknesses, and it emphasizes exploratory data techniques which sociologists will find useful in their day-to-day research. The primary characteristics of exploratory techniques discussed by the authors are simplicity, resistance and elucidation. Its coverage is from basic statistics up to multiple regression and two-way anova. The inter-relationship between exploratory and confirmatory techniques is stressed, and, through the alternating presentation of each, the students learn to master data analysis: to be and to feel in control. |
| john tukey exploratory data analysis: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| john tukey exploratory data analysis: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| john tukey exploratory data analysis: Adventures of a Statistician Mark Lorenzo, 2018-08-22 Meet John W. Tukey, one of the most consequential statisticians and original thinkers of the twentieth century. Growing up one hundred years ago in New Bedford, Massachusetts, a large coastal town primarily known for its commercial fishing and textile industries, John Wilder Tukey quickly showed himself to be a child prodigy. The son of educated parents whose high school classmates voted them most likely to give birth to a genius, he learned to read on his own by three years of age, mastered using a hand-crack desk calculator to speed up arithmetical calculations shortly thereafter, and was poring through technical journals in the New Bedford Free Public Library by the time he was a teenager. Homeschooled until being admitted to Brown University, Tukey majored in chemistry there--even as he spent countless hours in the university library compiling lists of statistical techniques on index cards, simply because he found them interesting and useful. With multiple degrees in hand, Tukey's next stop was Princeton University, where his interests shifted to mathematics. After earning a doctorate in topology, an especially abstract branch of mathematics, Princeton retained him as a lecturer. But with the United States poised to enter World War II, Tukey joined the Fire Control Research Office (FCRO), where he was exposed to a set of life-and-death problems that bore little resemblance to abstract mathematics: namely, calculating the trajectories of artillery and ballistics and the motions of rocket powder, working with stereoscopic height and range finders, and improving the Boeing B-29 Superfortress bomber. With the stakes never higher, a chance encounter during the war with a fellow polymath and unconventional thinker twenty years his senior set the course for the rest of Tukey's professional life--as well as changing the field of statistics forever. In Adventures of a Statistician, author Mark Jones Lorenzo chronicles John Tukey's life and times, from his decades spent at Princeton as a teacher and administrator and also at AT&T's Bell Laboratories as a scientific generalist; to his development of the fast Fourier transform (FFT) algorithm, which launched a revolution in digital signal processing; to his innovative ideas in displaying and summarizing data, such as with the intuitive stem-and-leaf plot and the interactive graphics of the PRIM-9 computer system; to his creation of exploratory data analysis, an approach to performing statistics he equated with detective work; to his intellectual war with sex researcher Alfred Kinsey over appropriate kinds of statistical sampling; to his productive yet sometimes strained relationships with fellow statisticians such as Ronald Fisher, George Box, and Erich Lehmann; to his enlightening friendship with the legendary physicist Richard Feynman; to his mentoring of dozens of doctoral students, many of whom went on to have highly successful careers in their own right; to his inventive use of language, having coined words like bit; to his development of sophisticated mathematical methods to detect underground nuclear explosions; to his groundbreaking work on the jackknife, multiple comparisons, robustness, and many other statistical techniques; and to his accomplishments in health and environmental regulation, U.S. census analysis, election forecasting, and public policy, among a host of other significant and impactful achievements. Nearly a decade in the making, Adventures of a Statistician is more than just the complete biography of John W. Tukey, perhaps the most revolutionary applied statistician of the past century. It's also a fascinating intellectual journey through the recent history of statistics as well. |
| john tukey exploratory data analysis: Selected Papers of Frederick Mosteller Stephen E. Fienberg, David C. Hoaglin, 2007-02-01 One of the best known statisticians of the 20th century, Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume collects 40 of his most original and influential papers, capturing the variety and depth of his writings. It is hoped that sharing these writings with a new generation of researchers will inspire them to build upon his insights and efforts. |
| john tukey exploratory data analysis: Statistical Models David A. Freedman, 2009-04-27 This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. |
| john tukey exploratory data analysis: Interactive Data Analysis Donald R. McNeil, 1977 Displays; Comparisons; Relations; Assays; Tables; Smoothing; Fitting. |
| john tukey exploratory data analysis: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| john tukey exploratory data analysis: Interactive and Dynamic Graphics for Data Analysis Dianne Cook, Deborah F. Swayne, 2007-12-12 This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: • An introduction to data visualization, explaining how it di?ers from other types of visualization. • Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. • An approach for exploring missing values in data. • An explanation of the use of these tools in cluster analysis and supervised classi?cation. • An overview of additional material available on the web. • A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high–quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort. |
| john tukey exploratory data analysis: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| john tukey exploratory data analysis: Computational Statistics Handbook with MATLAB Wendy L. Martinez, Angel R. Martinez, 2007-12-20 As with the bestselling first edition, Computational Statistics Handbook with MATLAB, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as |
| john tukey exploratory data analysis: Harness Oil and Gas Big Data with Analytics Keith R. Holdaway, 2014-05-27 Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits. |
| john tukey exploratory data analysis: Graphical Exploratory Data Analysis S. H. C. DuToit, A. G. W. Steyn, R. H. Stumpf, 2012-12-06 Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This realization has emerged from many years of experience in teaching students, in research, and especially from engaging in statistical consulting work in a variety of subject fields. Consequently, we were somewhat surprised to discover that a comprehen sive, yet simple presentation of graphical exploratory techniques for the data analyst was not available. Generally books on the subject were either too incomplete, stopping at a histogram or pie chart, or were too technical and specialized and not linked to readily available computer programs. Many of these graphical techniques have furthermore only recently appeared in statis tical journals and are thus not easily accessible to the statistically unsophis ticated data analyst. This book, therefore, attempts to give a sound overview of most of the well-known and widely used methods of analyzing and portraying data graph ically. Throughout the book the emphasis is on exploratory techniques. Real izing the futility of presenting these methods without the necessary computer programs to actually perform them, we endeavored to provide working com puter programs in almost every case. Graphic representations are illustrated throughout by making use of real-life data. Two such data sets are frequently used throughout the text. In realizing the aims set out above we avoided intricate theoretical derivations and explanations but we nevertheless are convinced that this book will be of inestimable value even to a trained statistician. |
| john tukey exploratory data analysis: Exploratory Data Analysis Walteburg Et Al, Eric Waltenburg, Sara Wiest, William Mclauchlan, 2012-08-30 eBook Version You will receive access to this electronic text via email after using the shopping cart above to complete your purchase. |
| john tukey exploratory data analysis: Algorithms for Data Science Brian Steele, John Chandler, Swarna Reddy, 2016-12-25 This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners. |
| john tukey exploratory data analysis: The Art of Data Science Roger D. Peng, Elizabeth Matsui, 2016-06-08 This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.--Leanpub.com. |
| john tukey exploratory data analysis: Scientific Analysis on the Pocket Calculator Jon M. Smith, 1977 |
| john tukey exploratory data analysis: Beautiful Visualization Julie Steele, Noah Iliinsky, 2010-04-23 Visualization is the graphic presentation of data -- portrayals meant to reveal complex information at a glance. Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding. This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world. Explore the importance of storytelling with a simple visualization exercise Learn how color conveys information that our brains recognize before we're fully aware of it Discover how the books we buy and the people we associate with reveal clues to our deeper selves Recognize a method to the madness of air travel with a visualization of civilian air traffic Find out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include: Nick Bilton,Michael E. Driscoll,Jonathan Feinberg,Danyel Fisher,Jessica Hagy,Gregor Hochmuth,Todd Holloway,Noah Iliinsky,Eddie Jabbour,Valdean Klump,Aaron Koblin,Robert Kosara,Valdis Krebs,JoAnn Kuchera-Morin et al.,Andrew Odewahn,Adam Perer,Anders Persson,Maximilian Schich,Matthias Shapiro,Julie Steele,Moritz Stefaner,Jer Thorp,Fernanda Viegas,Martin Wattenberg,and Michael Young. |
| john tukey exploratory data analysis: ggplot2 Hadley Wickham, 2009-10-03 Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures |
| john tukey exploratory data analysis: Interactive Graphics for Data Analysis Martin Theus, Simon Urbanek, 2008-10-24 Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets.Fundamentals of Interactive Statistical GraphicsThe first part of the book summarizes principles and methodology, demons |
Exploratory Data Analysis Tukey - archive.southernwv.edu
Exploratory Data Analysis: An Introduction to Selected Methods John W. Tukey, of Princeton University and Bell Labora-tories, has formulated a systematic approach to exploratory data …
Exploratory Data Analysis - GitHub Pages
Exploratory Data Analysis. Roger D. Peng Stephanie C. Hicks. Advanced Data Science Term 1 2019. “Far better an approximate answer to the right question, which is often vague, than an …
John W. Tukey and Data Analysis - JSTOR
To many in statistics and other fields John Tukey may be best known for Exploratory Data Analysis (EDA), which first appeared in print in 1970, but data analysis played a major role in …
Exploratory Data Analysis: An Introduction to Selected Methods
John W. Tukey, of Princeton University and Bell Labora- tories, has formulated a systematic approach to exploratory data analysis (EDA) that promises to bring this phase of data analysis …
EXPLORATORY DATA ANALYSIS - theta.edu.pl
The second VLSS was designed to provide an up-to-date source of data on households to be used in policy design, monitoring of living standards and evaluation of policies and programs.
John W. Tukey, Exploratory Data Analysis. Don Mills: Addison …
Interactive Data Analysis is built around a set of computer programs implementing various exploratory methods and the use of these programs is illustrated in a sequence of examples.
Data analysis, exploratory - University of California, Berkeley
John W. Tukey, the definer of the phrase . explor-atory data analysis (EDA), made remarkable con-tributions to the physical and social sciences. In the matter of data analysis, his …
TUKEY, JOHN WILDER - University of California, Berkeley
John Tukey was one of the great statistical scientists of the twentieth century. He introduced algorithms, concepts, language, philos-ophy, and techniques. He made important contributions …
Exploratory Data Analysis By John Tukey (2024)
John Tukey's contributions to exploratory data analysis have fundamentally changed how we approach data. His emphasis on visualization, iteration, and robust methods remains as …
Exploratory Data Analysis John Tukey (book)
pioneered by the influential statistician John Tukey, comes in. This post dives deep into the world of EDA, exploring its core principles, techniques, and enduring relevance, all through the lens …
John Tukey Exploratory Data Analysis - wclc2019.iaslc.org
Exploratory Data Analysis: An Introduction to Selected Methods John W. Tukey, of Princeton University and Bell Labora- tories, has formulated a systematic approach to exploratory data …
Introduction to Tukey (1962) The Future of Data Analysis - Springer
"The Future of Data Analysis" anticipated the general acceptance of exploratory analysis within statistics, as reflected by a wealth of literature on exploratory topics as well as the inclusion in …
Exploratory Data Analysis - Springer
The objective is to make a (possibly large) collection of observations easier for a brain to manage and understand. Accordingly (see Tukey 1977, v): EDA aims to simplify descriptions to make …
Statistical Science John W. Tukey and Data Analysis - Project …
To many in statistics and other fields John Tukey may be best known for Exploratory Data Analysis (EDA), which first appeared in print in 1970, but data analysis played a major role in …
Chapter 5: Exploratory Data Analysis - mjandrews.org
In his famous 1977 book Exploratory Data Analysis, John Tukey describes exploratory data analysis as detective work. He likens the data analyst to police investigators who look for and …
The Future of Data Analysis - JSTOR
THE FUTURE OF DATA ANALYSIS' BY JOHN W. TuKEY. Princeton University and BeU Telephone Laboratories. I. General Considerations 2. 1. Introduction 2. 2. Special growth …
John Tukey Exploratory Data Analysis - ttlc2020.iaslc.org
Exploratory Data Analysis - SpringerLink Exploratory data analysis is a set of techniques that have been principally developed by Tukey, John Wilder since 1970. The philosophy behind …
John Wilder Tukey. 16 June 1915 26 July 2000 - Department of …
He popularized spectrum analysis as a way of studying stationary time series, he promoted exploratory data analysis at a time when the subject was not academically respectable, and he …
Exploratory Data Analysis: New Tools for the Analysis of Empirical Data
exploratory methods that either appear in EDA or are based on Tukey's notions, and endeavor to place these procedures in a context that clarifies the commonalities they share with traditional …
N.N. Vorob'ev .James W. Friedman - JSTOR
Tukey defines exploratory data analysis as numerical, counting, and graphical techniques applied to data to reveal what the data seem to say. The user of exploratory data analysis plays the …
Exploratory Data Analysis Tukey - archive.southernwv.edu
Exploratory Data Analysis: An Introduction to Selected Methods John W. Tukey, of Princeton University and Bell Labora-tories, has formulated a systematic approach to exploratory data …
Exploratory Data Analysis - GitHub Pages
Exploratory Data Analysis. Roger D. Peng Stephanie C. Hicks. Advanced Data Science Term 1 2019. “Far better an approximate answer to the right question, which is often vague, than an …
John W. Tukey and Data Analysis - JSTOR
To many in statistics and other fields John Tukey may be best known for Exploratory Data Analysis (EDA), which first appeared in print in 1970, but data analysis played a major role in …
Exploratory Data Analysis: An Introduction to Selected Methods
John W. Tukey, of Princeton University and Bell Labora- tories, has formulated a systematic approach to exploratory data analysis (EDA) that promises to bring this phase of data analysis …
EXPLORATORY DATA ANALYSIS - theta.edu.pl
The second VLSS was designed to provide an up-to-date source of data on households to be used in policy design, monitoring of living standards and evaluation of policies and programs.
John W. Tukey, Exploratory Data Analysis. Don Mills: Addison …
Interactive Data Analysis is built around a set of computer programs implementing various exploratory methods and the use of these programs is illustrated in a sequence of examples.
Data analysis, exploratory - University of California, Berkeley
John W. Tukey, the definer of the phrase . explor-atory data analysis (EDA), made remarkable con-tributions to the physical and social sciences. In the matter of data analysis, his …
TUKEY, JOHN WILDER - University of California, Berkeley
John Tukey was one of the great statistical scientists of the twentieth century. He introduced algorithms, concepts, language, philos-ophy, and techniques. He made important contributions …
Exploratory Data Analysis By John Tukey (2024)
John Tukey's contributions to exploratory data analysis have fundamentally changed how we approach data. His emphasis on visualization, iteration, and robust methods remains as …
Exploratory Data Analysis John Tukey (book)
pioneered by the influential statistician John Tukey, comes in. This post dives deep into the world of EDA, exploring its core principles, techniques, and enduring relevance, all through the lens …
John Tukey Exploratory Data Analysis - wclc2019.iaslc.org
Exploratory Data Analysis: An Introduction to Selected Methods John W. Tukey, of Princeton University and Bell Labora- tories, has formulated a systematic approach to exploratory data …
Introduction to Tukey (1962) The Future of Data Analysis - Springer
"The Future of Data Analysis" anticipated the general acceptance of exploratory analysis within statistics, as reflected by a wealth of literature on exploratory topics as well as the inclusion in …
Exploratory Data Analysis - Springer
The objective is to make a (possibly large) collection of observations easier for a brain to manage and understand. Accordingly (see Tukey 1977, v): EDA aims to simplify descriptions to make …
Statistical Science John W. Tukey and Data Analysis - Project …
To many in statistics and other fields John Tukey may be best known for Exploratory Data Analysis (EDA), which first appeared in print in 1970, but data analysis played a major role in …
Chapter 5: Exploratory Data Analysis - mjandrews.org
In his famous 1977 book Exploratory Data Analysis, John Tukey describes exploratory data analysis as detective work. He likens the data analyst to police investigators who look for and …
The Future of Data Analysis - JSTOR
THE FUTURE OF DATA ANALYSIS' BY JOHN W. TuKEY. Princeton University and BeU Telephone Laboratories. I. General Considerations 2. 1. Introduction 2. 2. Special growth …
John Tukey Exploratory Data Analysis - ttlc2020.iaslc.org
Exploratory Data Analysis - SpringerLink Exploratory data analysis is a set of techniques that have been principally developed by Tukey, John Wilder since 1970. The philosophy behind …
John Wilder Tukey. 16 June 1915 26 July 2000 - Department of …
He popularized spectrum analysis as a way of studying stationary time series, he promoted exploratory data analysis at a time when the subject was not academically respectable, and he …
Exploratory Data Analysis: New Tools for the Analysis of Empirical Data
exploratory methods that either appear in EDA or are based on Tukey's notions, and endeavor to place these procedures in a context that clarifies the commonalities they share with traditional …
N.N. Vorob'ev .James W. Friedman - JSTOR
Tukey defines exploratory data analysis as numerical, counting, and graphical techniques applied to data to reveal what the data seem to say. The user of exploratory data analysis plays the …
