Exploratory Data Analysis


Author: John Wilder Tukey
Publisher: Pearson College Division
ISBN: N.A
Category: Mathematics
Page: 688
View: 2381

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Scratching down numbers (stem-and-leaf); Schematic summaries (pictures and numbers); Easy re-expression; Effective comparison (including well-chosen expresion); Plots of relationship; Straightening out plots (using three points); Smoothing sequences; Optional sections for chapter 7; Parallel and wandering schematic plots; Delineations of batches of points; Using two-way analyses; Making two-way analyses; Advances fits; Three-way fits; Looking in two or more ways at batches of points; Counted fractions; Better smoothing; Counts in bin after bin; Product-ratio plots; Shapes of distribution; Mathematical distributions; Postscript.

Exploratory Data Analysis with MATLAB, Third Edition


Author: Wendy L. Martinez,Angel R. Martinez,Jeffrey Solka
Publisher: CRC Press
ISBN: 1315349841
Category: Mathematics
Page: 590
View: 7074

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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

Exploratory Data Analysis


Author: Frederick Hartwig,Brian E. Dearing
Publisher: SAGE
ISBN: 9780803913707
Category: Mathematics
Page: 83
View: 3677

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An introduction to the underlying principles, central concepts, and basic techniques for conducting and understanding exploratory data analysis -- with numerous social science examples.

Exploratory Data Analysis: An Introduction to Data Analysis Using SAS


Author: Patricia Cerrito
Publisher: Lulu.com
ISBN: 1435705424
Category: Science
Page: 272
View: 4998

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This is an introductory text on how to investigate datasets. It is intended to be a practical text for those who need to research large datasets. Therefore, it does not follow the standard contents for more typical introductory statistics textbooks. When you complete the material, you will be able to work with your data using data visualization and regression in order to make sense of it, and to use your findings to make decisions. The book makes use of the statistical software, SAS, and its menu system SAS Enterprise Guide. This can be used as a stand alone text, or as a supplementary text to a more standard course. There are some datasets to accompany this text. ID# 1640751, Data for Exploratory Data Analysis.

Understanding robust and exploratory data analysis


Author: David Caster Hoaglin,Frederick Mosteller,John Wilder Tukey
Publisher: Wiley-Interscience
ISBN: N.A
Category: Business & Economics
Page: 447
View: 6035

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The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensiveeditions, Wiley hopes to extend the life of these important works by making themavailable to future generations of mathematicians and scientists. Currently available in the Series: T.W. Anderson The Statistical Analysis of Time Series T.S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Robert G. Bartle The Elements of Integration and Lebesgue Measure George E. P. Box & Norman R. Draper Evolutionary Operation: A Statistical Method for Process Improvement George E. P. Box & George C. Tiao Bayesian Inference in Statistical Analysis R. W. Carter Finite Groups of Lie Type: Conjugacy Classes and Complex Characters R. W. Carter Simple Groups of Lie Type William G. Cochran & Gertrude M. Cox Experimental Designs, Second Edition Richard Courant Differential and Integral Calculus, Volume I Richard Courant Differential and Integral Calculus, Volume II Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume I Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume II D. R. Cox Planning of Experiments Harold S. M. Coxeter Introduction to Geometry, Second Edition Charles W. Curtis & Irving Reiner Representation Theory of Finite Groups and Associative Algebras Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume I Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume II Cuthbert Daniel Fitting Equations to Data: Computer Analysis of Multifactor Data, Second Edition Bruno de Finetti Theory of Probability, Volume I Bruno de Finetti Theory of Probability, Volume 2 W. Edwards Deming Sample Design in Business Research Amos de Shalit & Herman Feshbach Theoretical Nuclear Physics, Volume 1— Nuclear Structure Harold F. Dodge & Harry G. Romig Sampling Inspection Tables: Single and Double Sampling J. L. Doob Stochastic Processes Nelson Dunford & Jacob T. Schwartz Linear Operators, Part One, General Theory Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Two, Spectral Theory—Self Adjoint Operators in Hilbert Space Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Three, Spectral Operators Regina C. Elandt-Johnson & Norman L. Johnson Survival Models and Data Analysis Herman Feshbach Theoretical Nuclear Physics: Nuclear Reactions Joseph L. Fleiss Design and Analysis of Clinical Experiments Bernard Friedman Lectures on Applications-Oriented Mathematics Phillip Griffiths & Joseph Harris Principles of Algebraic Geometry Gerald J. Hahn & Samuel S. Shapiro Statistical Models in Engineering Marshall Hall, Jr. Combinatorial Theory, Second Edition Morris H. Hansen, William N. Hurwitz & William G. Madow Sample Survey Methods and Theory, Volume I—Methods and Applications Morris H. Hansen, William N. Hurwitz & William G. Madow Sample Survey Methods and Theory, Volume II—Theory Peter Henrici Applied and Computational Complex Analysis, Volume 1—Power Series—Integration—Conformal Mapping—Location of Zeros Peter Henrici Applied and Computational Complex Analysis, Volume 2—Special Functions—Integral Transforms—Asymptotics—Continued Fractions Peter Henrici Applied and Computational Complex Analysis, Volume 3—Discrete Fourier Analysis—Cauchy Integrals—Construction of Conformal Maps—Univalent Functions Peter Hilton & Yel-Chiang Wu A Course in Modern Algebra David C. Hoaglin, Frederick Mosteller & John W. Tukey Understanding Robust and Exploratory Data Analysis Harry Hochstadt Integral Equations Leslie Kish Survey Sampling Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume I Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume 2 Erwin O. Kreyszig Introductory Functional Analysis with Applications William H. Louisell Quantum Statistical Properties of Radiation Rupert G. Miller Jr. Survival Analysis Ali Hasan Nayfeh Introduction to Perturbation Techniques Ali Hasan Nayfeh & Dean T. Mook Nonlinear Oscillations Emanuel Parzen Modern Probability Theory & Its Applications P. M. Prenter Splines and Variational Methods Howard Raiffa & Robert Schlaifer Applied Statistical Decision Theory Walter Rudin Fourier Analysis on Groups Lawrence S. Schulman Techniques and Applications of Path Integration Shayle R. Searle Linear Models I. H. Segel Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems C. L. Siegel Topics in Complex Function Theory, Volume I—Elliptic Functions and Uniformization Theory C. L. Siegel Topics in Complex Function Theory, Volume II—Automorphic and Abelian Integrals C. L. Siegel Topics in Complex Function Theory, Volume III—Abelian Functions and Modular Functions of Several Variables L. Spitzer Physical Processes in the Interstellar Medium J. J. Stoker Differential Geometry J. J. Stoker Water Waves: The Mathematical Theory with Applications J. J. Stoker Nonlinear Vibrations in Mechanical and ElectricalSystems Richard Zallen The Physics of Amorphous Solids Arnold Zellner Introduction to Bayesian Inference in Econometrics

Think Stats


Author: Allen B. Downey
Publisher: "O'Reilly Media, Inc."
ISBN: 1491907371
Category: Computers
Page: 226
View: 9099

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If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction 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. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. 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

Graphical Exploratory Data Analysis


Author: S. H. C. DuToit,A. G. W. Steyn,R. H. Stumpf
Publisher: Springer Science & Business Media
ISBN: 1461249503
Category: Mathematics
Page: 314
View: 2415

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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.

Making Sense of Data I

A Practical Guide to Exploratory Data Analysis and Data Mining
Author: Glenn J. Myatt,Wayne P. Johnson
Publisher: John Wiley & Sons
ISBN: 1118422104
Category: Mathematics
Page: 248
View: 9732

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Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Exploratory Data Analysis Using Fisher Information


Author: Roy Frieden,Robert A. Gatenby
Publisher: Springer Science & Business Media
ISBN: 9781846287770
Category: Computers
Page: 363
View: 8176

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This book uses a mathematical approach to deriving the laws of science and technology, based upon the concept of Fisher information. The approach that follows from these ideas is called the principle of Extreme Physical Information (EPI). The authors show how to use EPI to determine the theoretical input/output laws of unknown systems. Will benefit readers whose math skill is at the level of an undergraduate science or engineering degree.

Exploratory Data Analysis in Business and Economics

An Introduction Using SPSS, Stata, and Excel
Author: Thomas Cleff
Publisher: Springer Science & Business Media
ISBN: 3319015176
Category: Business & Economics
Page: 215
View: 4539

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In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in economic analysis, decision-making and business planning. This textbook, “Exploratory Data Analysis in Business and Economics”, aims to familiarise students of economics and business as well as practitioners in firms with the basic principles, techniques, and applications of descriptive statistics and data analysis. Drawing on practical examples from business settings, it demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA, thus treating all of the topics typically covered in a university course on descriptive statistics. The German edition of this textbook is one of the “bestsellers” on the German market for literature in statistics.

Exploratory and Multivariate Data Analysis


Author: Michel Jambu
Publisher: Elsevier
ISBN: 0080923674
Category: Mathematics
Page: 432
View: 7314

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With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques. Key Features * Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones * Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques, illustrated with concrete and understandable examples * Includes a modern and up-to-date description of clustering algorithms with many properties which gives a new role of clustering in data analysis techniques

Exploratory Data Analysis

A Primer for Undergraduates
Author: Eric N Waltenburg,Sara Wiest,William Mclauchlan
Publisher: N.A
ISBN: 9781465200600
Category: Mathematics
Page: 120
View: 3058

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Multivariate Exploratory Data Analysis

A Perspective on Exploratory Factor Analysis
Author: Allen Yates
Publisher: SUNY Press
ISBN: 1438424566
Category: Business & Economics
Page: N.A
View: 8358

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In an exciting return to the roots of factor analysis, Allen Yates reviews its early history to clarify original objectives created by its discoverers and early developers. He then shows how computers can be used to accomplish the goals established by these early visionaries, while taking into account modern developments in the field of statistics that legitimize exploratory data analysis as a technique of discovery. The book presents a unique perspective on all phases of exploratory factor analysis. In doing so, the popular objectives of the method are literally turned upside down both at the stage where the model is being fitted to data and in the subsequent stage of simple structure transformation for meaningful interpretation. What results is a fully integrated approach to exploratory analysis of associations among observed variables, revealing underlying structure in a totally new and much more invariant manner than ever before possible.

Exploratory Data Analysis Using R


Author: Ronald K. Pearson
Publisher: CRC Press
ISBN: 0429847041
Category: Business & Economics
Page: 548
View: 5464

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Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

Exploratory Data Analysis in Empirical Research

Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14–16, 2001
Author: Manfred Schwaiger,Otto Opitz
Publisher: Springer Science & Business Media
ISBN: 364255721X
Category: Computers
Page: 536
View: 3726

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This volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The reader will find numerous ideas and examples for cross disciplinary applications of classification and data analysis methods in fields such as data and web mining, medicine and biological sciences as well as marketing, finance and management sciences.

Exploratory Data Mining and Data Cleaning


Author: Tamraparni Dasu,Theodore Johnson
Publisher: John Wiley & Sons
ISBN: 0471458643
Category: Mathematics
Page: 203
View: 5595

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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.

Exploratory Multivariate Analysis by Example Using R, Second Edition


Author: Francois Husson,Sebastien Le,Jérôme Pagès
Publisher: CRC Press
ISBN: 1315301865
Category: Mathematics
Page: 262
View: 2637

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Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors. The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.

Age, Time, and Fertility

Applications of Exploratory Data Analysis
Author: Mary B. Breckenridge
Publisher: Elsevier
ISBN: 1483264769
Category: Social Science
Page: 348
View: 7375

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Age, Time, and Fertility: Applications of Exploratory Data Analysis describes change in the age pattern of fertility that responds to a specific need in making fertility comparisons across time and place. This book discusses a modeling process based on Tukey's exploratory data analysis (EDA) methods, which is proved very effective in other fields for detecting underlying patterns, even in flawed data. The first part of this text provides an introduction to the philosophy and tools of EDA and to the data analyzed, examining in detail the process of developing and standardizing the closely fitting, few-parameter descriptions of demographic change in time sequence. The rest of the chapters examine the results and applications of fertility modeling and establish relations between change in the age pattern of fertility and level of fertility. This publication is intended for those interested in the measures and methods of fertility change that can be applied to demographic data.