Exploratory Data Analysis Using R

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Publisher : CRC Press
ISBN 13 : 0429847041
Total Pages : 563 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis Exploratory Data Analysis Using R by : Ronald K. Pearson

Download or read book Exploratory Data Analysis Using R written by Ronald K. Pearson and published by CRC Press. This book was released on 2018-05-04 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).

Hands-On Exploratory Data Analysis with R

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Author :
Publisher : Packt Publishing Ltd
ISBN 13 : 1789802083
Total Pages : 254 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Hands-On Exploratory Data Analysis with R by : Radhika Datar

Download or read book Hands-On Exploratory Data Analysis with R written by Radhika Datar and published by Packt Publishing Ltd. This book was released on 2019-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

R for Data Science

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Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Exploratory Data Analysis with R

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Author :
Publisher : Lulu.com
ISBN 13 : 1365060063
Total Pages : 210 pages
Book Rating : 4.3/5 (65 download)

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Book Synopsis Exploratory Data Analysis with R by : Roger Peng

Download or read book Exploratory Data Analysis with R written by Roger Peng and published by Lulu.com. This book was released on 2016 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the date you have. We will cover in detail the plotting systems in R as well as some of the basic principles of contructing informative data graphics. We will also cover some of the common multivariate statistical techniques uses to visualize high-dimensional data. Some of the topics we cover are making exploratory graphs, principles of analytic graphics, plotting systems and graphics devices in R, the base and ggplot2 plotting systems in R, clustering methods, and dimension reduction techniques. (Quelle: buchcover).

Exploratory Multivariate Analysis by Example Using R

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Publisher : CRC Press
ISBN 13 : 1315301865
Total Pages : 263 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Exploratory Multivariate Analysis by Example Using R by : Francois Husson

Download or read book Exploratory Multivariate Analysis by Example Using R written by Francois Husson and published by CRC Press. This book was released on 2017-04-25 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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) a

Exploratory Data Analysis with MATLAB

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Publisher : CRC Press
ISBN 13 : 1315349841
Total Pages : 589 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Exploratory Data Analysis with MATLAB by : Wendy L. Martinez

Download or read book Exploratory Data Analysis with MATLAB written by Wendy L. Martinez and published by CRC Press. This book was released on 2017-08-07 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Secondary Analysis of Electronic Health Records

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Author :
Publisher : Springer
ISBN 13 : 3319437429
Total Pages : 435 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Secondary Analysis of Electronic Health Records by : MIT Critical Data

Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Modern Statistics with R

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Publisher :
ISBN 13 : 9781032497457
Total Pages : 0 pages
Book Rating : 4.4/5 (974 download)

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Book Synopsis Modern Statistics with R by : Måns Thulin

Download or read book Modern Statistics with R written by Måns Thulin and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.

Exploratory Data Analytics for Healthcare

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Publisher : CRC Press
ISBN 13 : 1000527018
Total Pages : 307 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Exploratory Data Analytics for Healthcare by : R. Lakshmana Kumar

Download or read book Exploratory Data Analytics for Healthcare written by R. Lakshmana Kumar and published by CRC Press. This book was released on 2021-12-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Practical Statistics for Data Scientists

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Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 322 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data Points

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Publisher : John Wiley & Sons
ISBN 13 : 1118654935
Total Pages : 8 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Data Points by : Nathan Yau

Download or read book Data Points written by Nathan Yau and published by John Wiley & Sons. This book was released on 2013-03-25 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fresh look at visualization from the author of Visualize This Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Shares intriguing ideas from Nathan Yau, author of Visualize This and creator of flowingdata.com, with over 66,000 subscribers Focuses on visualization, data graphics that help viewers see trends and patterns they might not otherwise see in a table Includes examples from the author's own illustrations, as well as from professionals in statistics, art, design, business, computer science, cartography, and more Examines standard rules across all visualization applications, then explores when and where you can break those rules Create visualizations that register at all levels, with Data Points: Visualization That Means Something.

Data Analysis for the Life Sciences with R

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Publisher : CRC Press
ISBN 13 : 1498775861
Total Pages : 537 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Data Analysis for the Life Sciences with R by : Rafael A. Irizarry

Download or read book Data Analysis for the Life Sciences with R written by Rafael A. Irizarry and published by CRC Press. This book was released on 2016-10-04 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Data Manipulation with R

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Publisher : Springer Science & Business Media
ISBN 13 : 0387747303
Total Pages : 158 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Data Manipulation with R by : Phil Spector

Download or read book Data Manipulation with R written by Phil Spector and published by Springer Science & Business Media. This book was released on 2008-03-19 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book.

Computational Genomics with R

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Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 463 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Genomics with R by : Altuna Akalin

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Understanding Robust and Exploratory Data Analysis

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Publisher : John Wiley & Sons
ISBN 13 : 0471384917
Total Pages : 484 pages
Book Rating : 4.4/5 (713 download)

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Book Synopsis Understanding Robust and Exploratory Data Analysis by : David C. Hoaglin

Download or read book Understanding Robust and Exploratory Data Analysis written by David C. Hoaglin and published by John Wiley & Sons. This book was released on 2000-06-02 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Graphical Data Analysis with R

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Publisher : CRC Press
ISBN 13 : 1498715249
Total Pages : 306 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Graphical Data Analysis with R by : Antony Unwin

Download or read book Graphical Data Analysis with R written by Antony Unwin and published by CRC Press. This book was released on 2015-03-25 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

Exploratory Data Analysis

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Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (926 download)

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Book Synopsis Exploratory Data Analysis by : John Wilder Tukey

Download or read book Exploratory Data Analysis written by John Wilder Tukey and published by . This book was released on 1970 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: