Author : Mark van der Loo
Publisher : John Wiley & Sons
Release Date : 2018-04-16
ISBN 10 : 9781118897157
Pages : 320 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (897 users download)
Download Statistical Data Cleaning with Applications in R by Mark van der Loo PDF/Ebook Free clicking on the below button will initiate the downloading process of Statistical Data Cleaning with Applications in R by Mark van der Loo. This book is available in ePub and PDF format with a single click unlimited downloads. A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning with Applications in R brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. Statistical Data Cleaning with Applications in R enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. This book can also be used as material for courses in both data cleaning and data analysis.
Download Statistical Shape Analysis by Ian L. Dryden PDF/Ebook Free clicking on the below button will initiate the downloading process of Statistical Shape Analysis by Ian L. Dryden. This book is available in ePub and PDF format with a single click unlimited downloads. Originally published as: Statistical shape analysis, 1998
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Download Handbook of Regression Analysis With Applications in R by Samprit Chatterjee PDF/Ebook Free clicking on the below button will initiate the downloading process of Handbook of Regression Analysis With Applications in R by Samprit Chatterjee. This book is available in ePub and PDF format with a single click unlimited downloads. Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors’ thorough treatments of “classic” regression analysis, this book covers two important and more advanced topics of time-to-event survival data and longitudinal and clustered data. Further, methods that have become prominent in the last 15-30 years that are designed for analyses on often-large data sets and can take advantage of ﬂexibility in modeling were not covered, including smoothing, tree- based, and regularization methods, all of which are increasingly becoming part of the data analysis toolkit. Examples are drawn from a wide variety of application areas using real data sets and all of the R code is provided. The book will be of interest to data scientists as well as in regression analysis courses at the graduate and undergraduate level.
Download Exploratory Data Mining and Data Cleaning by Tamraparni Dasu PDF/Ebook Free clicking on the below button will initiate the downloading process of Exploratory Data Mining and Data Cleaning by Tamraparni Dasu. This book is available in ePub and PDF format with a single click unlimited downloads. Written for practitioners of data mining, data cleaning anddatabase management. Presents a technical treatment of data quality includingprocess, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through aniterative data exploration loop and incorporation of domainknowledge. Addresses methods of detecting, quantifying and correcting dataquality issues that can have a significant impact on findings anddecisions, using commercially available tools as well as newalgorithmic approaches. Uses case studies to illustrate applications in real lifescenarios. Highlights new approaches and methodologies, such as theDataSphere space partitioning and summary based analysistechniques. Exploratory Data Mining and Data Cleaning will serve as animportant reference for serious data analysts who need to analyzelarge amounts of unfamiliar data, managers of operations databases,and students in undergraduate or graduate level courses dealingwith large scale data analys is and data mining.
Download Handbook of Statistics by PDF/Ebook Free clicking on the below button will initiate the downloading process of Handbook of Statistics by . This book is available in ePub and PDF format with a single click unlimited downloads. R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl. The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered. Addresses data examples that can be downloaded directly from the R website No other source is needed to gain practical experience Focus on the essentials in graphical outlays
Download A Primer on Experiments with Mixtures by John A. Cornell PDF/Ebook Free clicking on the below button will initiate the downloading process of A Primer on Experiments with Mixtures by John A. Cornell. This book is available in ePub and PDF format with a single click unlimited downloads. The concise yet authoritative presentation of key techniques for basic mixtures experiments Inspired by the author's bestselling advanced book on the topic, A Primer on Experiments with Mixtures provides an introductory presentation of the key principles behind experimenting with mixtures. Outlining useful techniques through an applied approach with examples from real research situations, the book supplies a comprehensive discussion of how to design and set up basic mixture experiments, then analyze the data and draw inferences from results. Drawing from his extensive experience teaching the topic at various levels, the author presents the mixture experiments in an easy-to-follow manner that is void of unnecessary formulas and theory. Succinct presentations explore key methods and techniques for carrying out basic mixture experiments, including: Designs and models for exploring the entire simplex factor space, with coverage of simplex-lattice and simplex-centroid designs, canonical polynomials, the plotting of individual residuals, and axial designs Multiple constraints on the component proportions in the form of lower and/or upper bounds, introducing L-Pseudocomponents, multicomponent constraints, and multiple lattice designs for major and minor component classifications Techniques for analyzing mixture data such as model reduction and screening components, as well as additional topics such as measuring the leverage of certain design points Models containing ratios of the components, Cox's mixture polynomials, and the fitting of a slack variable model A review of least squares and the analysis of variance for fitting data Each chapter concludes with a summary and appendices with details on the technical aspects of the material. Throughout the book, exercise sets with selected answers allow readers to test their comprehension of the material, and References and Recommended Reading sections outline further resources for study of the presented topics. A Primer on Experiments with Mixtures is an excellent book for one-semester courses on mixture designs and can also serve as a supplement for design of experiments courses at the upper-undergraduate and graduate levels. It is also a suitable reference for practitioners and researchers who have an interest in experiments with mixtures and would like to learn more about the related mixture designs and models.
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Download An Introduction to Probability and Statistics by Vijay K. Rohatgi PDF/Ebook Free clicking on the below button will initiate the downloading process of An Introduction to Probability and Statistics by Vijay K. Rohatgi. This book is available in ePub and PDF format with a single click unlimited downloads. This Third Edition provides a solid and well-balancedintroduction to probability theory and mathematicalstatistics. The book is divided into three parts: Chapters1-6 form the core of probability fundamentals and foundations;Chapters 7-11 cover statistics inference; and the remainingchapters focus on special topics. For course sequences thatseparate probability and mathematics statistics, the first part ofthe book can be used for a course in probability theory, followedby a course in mathematical statistics based on the second part,and possibly, one or more chapters on special topics. Thebook contains over 550 problems, 350 worked-out examples, and 200side notes for reader reference. Numerous figures have beenadded to illustrate examples and proofs, and answers to selectproblems are now included. Many parts of the book haveundergone substantial rewriting, and the book has also beenreorganized. Chapters 6 and 7 have been interchanged to emphasizethe role of asymptotics in statistics, and the new Chapter 7contains all of the needed basic material on asymptotics. Chapter 6 also includes new material on resampling, specificallybootstrap. The new Further Results chapter include someestimation procedures such as M-estimatesand bootstrapping. A new chapter on regression analysishas also been added and contains sections on linear regression,multiple regression, subset regression, logistic regression, andPoisson regression.
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