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Otto Wildi Data Analysis in Vegetation Ecology

Otto Wildi Data Analysis in Vegetation Ecology


Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes known by ecologists often find their counterpart in mathematical operations and functions. The first chapters introduce the elementary concepts and operations and relate them to real-world phenomena and problems. Later chapters concentrate on combinations of methods to reveal surprising features in data sets. Showing how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps.

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Martin Kent Vegetation Description and Data Analysis. A Practical Approach


Vegetation Description and Data Analysis: A Practical Approach, Second Edition is a fully revised and up-dated edition of this key text. The book takes account of recent advances in the field whilst retaining the original reader-friendly approach to the coverage of vegetation description and multivariate analysis in the context of vegetation data and plant ecology. Since the publication of the hugely popular first edition there have been significant developments in computer hardware and software, new key journals have been established in the field and scope and application of vegetation description and analysis has become a truly global field. This new edition includes full coverage of new developments and technologies. This contemporary and comprehensive edition of this well-known and respected textbook will prove invaluable to undergraduate and graduate students in biological sciences, environmental science, geography, botany, agriculture, forestry and biological conservation. Fully international approach Includes illustrative case studies throughout Now with new material on: the nature of plant communities; transitional areas between plant communities; induction and deduction of plant ecology; diversity indices and dominance diversity curves; multivariate analysis in ecology. Accessible, reader-friendly style Now with new and improved illustrations

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Charles D. Bonham Measurements for Terrestrial Vegetation


Measurements for Terrestrial Vegetation, 2nd Edition presents up-to-date methods for analyzing species frequency, plant cover, density and biomass data. Each method is presented in detail with a full discussion of its strengths and weaknesses from field applications through statistical characteristics of bias and use of the correct probability distribution to describe and analyze data. This practical book also covers the use of satellite imagery to obtain measurement data on cover, density and biomass. Field data collection includes current applications of statistical sampling and analysis designs that should be used to obtain and analyze these data. This new and thoroughly updated edition of a classic text will be essential reading for everyone involved in measuring and assessing vegetation and plant biomass, including researchers and practitioners in vegetation science, plant ecology, forestry, global change scientists and conservation scientists. Provides a comprehensive catalogue of sampling, surveying and measuring techniques in vegetation science Updated to include new technologies and developments in the field New coverage of prediction models for large areas, including satellite mapping and remote sensing techniques Includes up-to-date applications of statistical sampling and analysis designs used to obtain and analyse data Reviews the strengths and weaknesses of each technique, allowing an informed choice of alternative approaches Clear diagrams to explain best-practice in methodology The companion website for this book can be found at www.wiley.com/go/bonham/measurements

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Yuichi Motai Data-Variant Kernel Analysis


Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.

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Bendat Julius S. Random Data. Analysis and Measurement Procedures


A timely update of the classic book on the theory and application of random data analysis First published in 1971, Random Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications. This Fourth Edition features coverage of new developments in random data management and analysis procedures that are applicable to a broad range of applied fields, from the aerospace and automotive industries to oceanographic and biomedical research. This new edition continues to maintain a balance of classic theory and novel techniques. The authors expand on the treatment of random data analysis theory, including derivations of key relationships in probability and random process theory. The book remains unique in its practical treatment of nonstationary data analysis and nonlinear system analysis, presenting the latest techniques on modern data acquisition, storage, conversion, and qualification of random data prior to its digital analysis. The Fourth Edition also includes: A new chapter on frequency domain techniques to model and identify nonlinear systems from measured input/output random data New material on the analysis of multiple-input/single-output linear models The latest recommended methods for data acquisition and processing of random data Important mathematical formulas to design experiments and evaluate results of random data analysis and measurement procedures Answers to the problem in each chapter Comprehensive and self-contained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upper-undergraduate and graduate level. It is also an insightful reference for engineers and scientists who use statistical methods to investigate and solve problems with dynamic data.

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Hyunjoung Lee Fundamentals of Big Data Network Analysis for Research and Industry


Fundamentals of Big Data Network Analysis for Research and Industry Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea Presents the methodology of big data analysis using examples from research and industry There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets. Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail. Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis. This book: Explains the basic concepts in understanding big data and filtering meaningful data Presents big data analysis within the networking perspective Features methodology applicable to research and industry Describes in detail the social relationship between big data and its implications Provides insight into identifying patterns and relationships between seemingly unrelated big data Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

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Lawrence S. Meyers Performing Data Analysis Using IBM SPSS


Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical output Descriptive statistics procedures Score distribution assumption evaluations Bivariate correlation Regressing (predicting) quantitative and categorical variables Survival analysis t Test ANOVA and ANCOVA Multivariate group differences Multidimensional scaling Cluster analysis Nonparametric procedures for frequency data Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.

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Michael J. Albers Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences


Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the data’s contextual situation. Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following: • The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis • A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data • Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends • Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems • A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods. Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and human–information interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University.

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Pawlowsky-Glahn Vera Compositional Data Analysis. Theory and Applications


It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Key Features: Reflects the state-of-the-art in compositional data analysis. Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures. Looks at advances in algebra and calculus on the simplex. Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics. Explores connections to correspondence analysis and the Dirichlet distribution. Presents a summary of three available software packages for compositional data analysis. Supported by an accompanying website featuring R code. Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data.

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Data Analysis in Vegetation Ecology: Amazon.de: Wildi ...

Data Analysis in Vegetation Ecology | Wildi, Otto | ISBN: 9781118384046 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Wildi, O: Data Analysis in Vegetation Ecology: Amazon.de ...

Wildi, O: Data Analysis in Vegetation Ecology | Wildi, Otto (Swiss Federal Research Institute WSL, Switzerland) | ISBN: 9781786394224 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Data Analysis in Vegetation Ecology - Otto Wildi ...

The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author's extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step-by-step case studies themselves using the ...

Data Analysis in Vegetation Ecology von Otto Wildi ...

The third edition of this popular graduate-level text introduces the reader to the investigation of vegetation systems with an emphasis on data analysis.

Data Analysis - download.e-bookshelf.de

Library of Congress Cataloging-in-Publication Data Wildi, Otto. Data analysis in vegetation ecology / Otto Wildi. pages cm Includes bibliographical references and index. ISBN 978-1-118-38404-6 (cloth) – ISBN 978-1-118-38403-9 (pbk.) 1. Plant communities–Data processing. 2. Plant communities–Mathematical models. 3. Plant ecology–Data processing. 4. Plant ecology–Mathematical models. I ...

Data Analysis in Vegetation Ecology - CABI.org

Data Analysis in Vegetation Ecology. 3rd Edition. By: Otto Wildi, Swiss Federal Research Institute WSL, Switzerland October 2017 | Paperback | 356 Pages | 9781786394224. October 2017 | ePDF 9781786394231 | ePub 9781786394248. Format. £39.99 | €55.00 | $65.00. Add to Basket | | Request Inspection Copy. Request e-Inspection Copy. Description Table of contents Readership Reviews. Description ...

Data Analysis in Vegetation Ecology (3rd ed.) by Wildi ...

Data Analysis in Vegetation Ecology (3rd ed.) by Otto Wildi. The 3rd edition of this popular textbook introduces the reader to the investigation of vegetation systems with an emphasis on data analysis. The book succinctly illustrates the various paths leading to high quality data suitable for pattern recognition, pattern testing, static and dynamic modelling and model testing including spatial ...

Data Analysis in Vegetation Ecology | Wiley Online Books

Otto Wildi is from the WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland.

Data Analysis in Vegetation Ecology - Otto Wildi - Google ...

2010 John Wiley Acer pseudoplatanus Analysis in Vegetation attributes axes centre classification computed contingency table correlation coefficient correlogram correspondence analysis Data Analysis data points data set defined definition dendrogram derived difficult Ecology Otto Wildi Eigenvalues elements environmental equation Euclidean distance example F-values factors field final ...

Data Analysis in Vegetation Ecology by Otto Wildi ...

Otto Wildi. ISBN. 9781118384039. Published. 10 / 05 / 2013. Binding. Paperback. Pages. 330. Dimensions. 157 x 235 x 16mm. The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author's extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only ...

Data analysis in vegetation ecology | Wildi, Otto | download

Main Data analysis in vegetation ecology. Data analysis in vegetation ecology Wildi, Otto. Year: 2017. Edition: 3rd edition. Language: english. Pages: 333 / 357. ISBN 10: 1786394243. ISBN 13: 978-1-78639-422-4. File: PDF, 8.25 MB. Preview. Send-to-Kindle or Email . Please login to your account first; Need help? Please read our short guide how to send a book to Kindle. Save for later. You may ...

Data Analysis in Vegetation Ecology by Otto Wildi

Data Analysis in Vegetation Ecology book. Read reviews from world’s largest community for readers. Evolving from years of teaching experience by one of t...

PD Dr. Otto Wildi - Mitarbeitende - WSL

otto.wildi(at)wsl.ch Eidg. Forschungsanstalt WSL Zürcherstrasse 111 8903 Birmensdorf. Standort. Birmensdorf Bi MG C 52 ... Patterns are recurring regularities, and pattern recognition is a target of data analysis in vegetation ecology. A proper sampling design is required to deliver data reflecting properties of the real system. Similarity patterns eventually occur in any sub-system, in ...

Wiley: Data Analysis in Vegetation Ecology, 2nd Edition ...

Otto Wildi. ISBN: 978-1-118-38403-9. 330 pages. May 2013, Wiley-Blackwell. Read an Excerpt ... Description. The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author’s extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how ...

Data Analysis in Vegetation Ecology (Taschenbuch), Otto ...

Data Analysis in Vegetation Ecology, Taschenbuch von Otto (Swiss Federal Research Institute WSL, Switzerland) Wildi bei hugendubel.de. Online bestellen oder in der Filiale abholen.

Data Analysis In Vegetation Ecology [PDF, EPUB EBOOK]

^ Data Analysis In Vegetation Ecology ^ Uploaded By Arthur Hailey, data analysis in vegetation ecology by otto wildi 2010 wiley blackwell the atrium southern gate chichester west sussex po19 8sq uk 211 pages hardcover and softcover isbn 978 0 470 the first edition of data analysis in vegetation ecology provided an accessible and thorough resource for evaluating plant ecology data based on the ...

Data Analysis in Vegetation Ecology - Otto Wildi - online ...

The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author s extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step-by-step case studies themselves using the ...

Otto Wildi: Data Analysis in Vegetation Ecology (eBook ...

Otto Wildi: Data Analysis in Vegetation Ecology - Sprache: Englisch. (eBook epub) - bei eBook.de. Hilfe +49 (0)40 4223 6096 Suche eBooks . Bestseller Neuerscheinungen Preishits ² eBooks verschenken . Biografien ...

Data Analysis in Vegetation Ecology - Otto Wildi - online ...

Evolving from years of teaching experience by one of the topexperts in vegetation ecology, Data Analysis in VegetationEcology aims to explain the background and basics ofmathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis,the underlying hypotheses, aims and points of views. It conveys themessage that each step in the ...

Data Analysis in Vegetation Ecology - Otto Wildi - online ...

Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views. It conveys the message that each step in ...

Data Analysis in Vegetation Ecology (English Edition ...

Data Analysis in Vegetation Ecology (English Edition) eBook: Wildi, Otto: Amazon.de: Kindle-Shop Wählen Sie Ihre Cookie-Einstellungen Wir verwenden Cookies und ähnliche Tools, um Ihr Einkaufserlebnis zu verbessern, um unsere Dienste anzubieten, um zu verstehen, wie die Kunden unsere Dienste nutzen, damit wir Verbesserungen vornehmen können, und um Werbung anzuzeigen.

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Amazon.com: Data Analysis in Vegetation Ecology (9781118384039): Wildi, Otto: Books ... Otto Wildi (Author) › Visit Amazon's Otto Wildi Page. Find all the books, read about the author, and more. See search results for this author. Are you an author? Learn about Author Central. Otto Wildi (Author) 3.4 out of 5 stars 3 ratings. ISBN-13: 978-1118384039. ISBN-10: 9781118384039. Why is ISBN ...

‎Data Analysis in Vegetation Ecology on Apple Books

Otto Wildi. $69.99; $69.99; Publisher Description. The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author’s extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the ...

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Otto Wildi, otto.wildi@wsl.ch References Wildi, O. 2013. Data Analysis in Vegetation Ecology. 2nd ed. Wiley-Blackwell, Chichester. Wildi, O. 2017. Data Analysis in Vegetation Ecology. 3rd ed. CABI, Oxfordshire, Boston. Examples # A typical and probably the most complex function is Mtab() that re-arranges

Data Analysis in Vegetation Ecology | 9781786394224 ...

Data Analysis in Vegetation Ecology von Otto Wildi und Verleger CABI. Sparen Sie bis zu 80% durch die Auswahl der eTextbook-Option für ISBN: 9781786394231, 1786394235. Die Druckversion dieses Lehrbuchs hat ISBN: 9781786394224, 1786394227.

Data Analysis In Vegetation Ecology PDF

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Data Analysis in Vegetation Ecology | Wiley Online Books

Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views.

Otto WILDI | Swiss Federal Institute for Forest, Snow and ...

Otto Wildi An ecological risk assessment is described for determining the adaptation potential of the approximately 11 000 Swiss Forest Inventory points (FIP) to a hypothetically changing climate.

Data Analysis in Vegetation Ecology (eBook, PDF) von Otto ...

Evolving from years of teaching experience by one of the topexperts in vegetation ecology, Data Analysis in VegetationEcology aims to explain the background and basics ofmathematical (mainly multivariate) analysis of vegetation data.

Data Analysis in Vegetation Ecology, 3rd Edition - Otto ...

All data sets and examples described in the book are available online and are written using the freely available statistical package R. This book will be of particular value to beginning graduate students and postdoctoral researchers of vegetation ecology, ecological data analysis, and ecological modelling, and experienced researchers needing a guide to new methods. A completely revised and ...

Otto Wildi - Info zur Person mit Bilder, News & Links ...

124 Ergebnisse zu Otto Wildi: Data Analysis, Analysis in Vegetation, Vegetation Ecology, Felix Kienast, WSL, Changing, ETH, Zürich

Data Analysis in Vegetation Ecology (eBook, PDF) von Otto ...

The first edition of Data Analysis in Vegetation Ecologyprovided an accessible and thorough resource for evaluating plantecology data, based on the authors extensive experience ofresearch and analysis in this field.

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Data Analysis in Vegetation Ecology - Kindle edition by Wildi, Otto. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Data Analysis in Vegetation Ecology.

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Evaluating the Predictive Power of Ordination Methods in Ecological Context . von Wildi, Otto Veröffentlicht in Mathematics (2018)

Johnson Wayne P. Making Sense of Data I. A Practical Guide to Exploratory Data Analysis and Data Mining


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.

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Steen Knudsen A Biologist's Guide to Analysis of DNA Microarray Data


A great introductory book that details reliable approaches to problems met in standard microarray data analyses. It provides examples of established approaches such as cluster analysis, function prediction, and principle component analysis. Discover real examples to illustrate the key concepts of data analysis. Written for those without any advanced background in math, statistics, or computer sciences, this book is essential for anyone interested in harnessing the immense potential of microarrays in biology and medicine.

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Galit Shmueli Information Quality


Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management. This book: Explains how to integrate the notions of goal, data, analysis and utility that are the main building blocks of data analysis within any domain. Presents a framework for integrating domain knowledge with data analysis. Provides a combination of both methodological and practical aspects of data analysis. Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects. Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys. Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website. This book will be beneficial for researchers in academia and in industry, analysts, consultants, and agencies that collect and analyse data as well as undergraduate and postgraduate courses involving data analysis.

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Группа авторов Data Analysis


The first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.

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Claudio Carpineto Concept Data Analysis


With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. Concept Data Analysis: Theory & Applications is the first book that provides a comprehensive treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The accompanying website allows you to gain a greater understanding of the principles covered in the book through actively working on the topics discussed. The three main areas explored are interactive mining of documents or collections of documents (including Web documents), automatic text ranking, and rule mining from structured data. The potentials of conceptual data analysis in the application areas being considered are further illustrated by two detailed case studies. Concept Data Analysis: Theory & Applications is essential for researchers active in information processing and management and industry practitioners who are interested in creating a commercial product for conceptual data analysis or developing content management applications.

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Группа авторов Categorical Data Analysis


Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Categorical Data Analysis was among those chosen. A valuable new edition of a standard reference «A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis.» –Statistics in Medicine on Categorical Data Analysis, First Edition The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis. Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of: Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects Stronger emphasis on logistic regression modeling of binary and multicategory data An appendix showing the use of SAS for conducting nearly all analyses in the book Prescriptions for how ordinal variables should be treated differently than nominal variables Discussion of exact small-sample procedures More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

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Alan Agresti Categorical Data Analysis


Praise for the Second Edition «A must-have book for anyone expecting to do research and/or applications in categorical data analysis.» —Statistics in Medicine «It is a total delight reading this book.» —Pharmaceutical Research «If you do any analysis of categorical data, this is an essential desktop reference.» —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

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Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics


A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning.

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Rupert G. Miller, Jr. Survival Analysis


A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.

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Nii O. Attoh-Okine Big Data and Differential Privacy


A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for railway track engineering, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering addresses common issues with the implementation of big data applications while exploring the limitations, advantages, and disadvantages of more conventional methods. In addition, the book provides a unifying approach to analyzing large volumes of data in railway track engineering using an array of proven methods and software technologies. Dr. Attoh-Okine considers some of today’s most notable applications and implementations and highlights when a particular method or algorithm is most appropriate. Throughout, the book presents numerous real-world examples to illustrate the latest railway engineering big data applications of predictive analytics, such as the Union Pacific Railroad’s use of big data to reduce train derailments, increase the velocity of shipments, and reduce emissions. In addition to providing an overview of the latest software tools used to analyze the large amount of data obtained by railways, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering: • Features a unified framework for handling large volumes of data in railway track engineering using predictive analytics, machine learning, and data mining • Explores issues of big data and differential privacy and discusses the various advantages and disadvantages of more conventional data analysis techniques • Implements big data applications while addressing common issues in railway track maintenance • Explores the advantages and pitfalls of data analysis software such as R and Spark, as well as the Apache™ Hadoop® data collection database and its popular implementation MapReduce Big Data and Differential Privacy is a valuable resource for researchers and professionals in transportation science, railway track engineering, design engineering, operations research, and railway planning and management. The book is also appropriate for graduate courses on data analysis and data mining, transportation science, operations research, and infrastructure management. NII ATTOH-OKINE, PhD, PE is Professor in the Department of Civil and Environmental Engineering at the University of Delaware. The author of over 70 journal articles, his main areas of research include big data and data science; computational intelligence; graphical models and belief functions; civil infrastructure systems; image and signal processing; resilience engineering; and railway track analysis. Dr. Attoh-Okine has edited five books in the areas of computational intelligence, infrastructure systems and has served as an Associate Editor of various ASCE and IEEE journals.

13029.7 RUR

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