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Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics
A Practical Guide for the Petroleum Geosciences

by Srikanta Mishra,Akhil Datta-Gupta

  • Publisher : Elsevier
  • Release : 2017-10-27
  • Pages : 250
  • ISBN : 0128032804
  • Language : En, Es, Fr & De
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Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications

Exam Prep for: Applied Statistical Modeling and Data Analytics

Exam Prep for: Applied Statistical Modeling and Data Analytics
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Applied Data Analysis and Modeling for Energy Engineers and Scientists

Applied Data Analysis and Modeling for Energy Engineers and Scientists
A Book

by T. Agami Reddy

  • Publisher : Springer Science & Business Media
  • Release : 2011-08-09
  • Pages : 430
  • ISBN : 9781441996138
  • Language : En, Es, Fr & De
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Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.

Applied Predictive Modeling

Applied Predictive Modeling
A Book

by Max Kuhn,Kjell Johnson

  • Publisher : Springer Science & Business Media
  • Release : 2013-05-17
  • Pages : 600
  • ISBN : 1461468493
  • Language : En, Es, Fr & De
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Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Advances in Statistical Models for Data Analysis

Advances in Statistical Models for Data Analysis
A Book

by Isabella Morlini,Tommaso Minerva,Maurizio Vichi

  • Publisher : Springer
  • Release : 2015-09-04
  • Pages : 268
  • ISBN : 3319173774
  • Language : En, Es, Fr & De
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This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

Learn R for Applied Statistics

Learn R for Applied Statistics
With Data Visualizations, Regressions, and Statistics

by Eric Goh Ming Hui

  • Publisher : Apress
  • Release : 2018-11-30
  • Pages : 243
  • ISBN : 1484242009
  • Language : En, Es, Fr & De
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Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

Recent Studies on Risk Analysis and Statistical Modeling

Recent Studies on Risk Analysis and Statistical Modeling
A Book

by Teresa A. Oliveira,Christos P. Kitsos,Amílcar Oliveira,Luís Grilo

  • Publisher : Springer
  • Release : 2018-08-22
  • Pages : 375
  • ISBN : 3319766058
  • Language : En, Es, Fr & De
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This book provides an overview of the latest developments in the field of risk analysis (RA). Statistical methodologies have long-since been employed as crucial decision support tools in RA. Thus, in the context of this new century, characterized by a variety of daily risks - from security to health risks - the importance of exploring theoretical and applied issues connecting RA and statistical modeling (SM) is self-evident. In addition to discussing the latest methodological advances in these areas, the book explores applications in a broad range of settings, such as medicine, biology, insurance, pharmacology and agriculture, while also fostering applications in newly emerging areas. This book is intended for graduate students as well as quantitative researchers in the area of RA.

Handbook of Big Data Analytics

Handbook of Big Data Analytics
A Book

by Wolfgang Karl Härdle,Henry Horng-Shing Lu,Xiaotong Shen

  • Publisher : Springer
  • Release : 2018-07-20
  • Pages : 538
  • ISBN : 3319182846
  • Language : En, Es, Fr & De
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Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

Applied Statistics in Business and Economics | Sixth Edition | SIE

Applied Statistics in Business and Economics | Sixth Edition | SIE
A Book

by David P. Doane,Lori E. Seward,Shovan Chowdhury

  • Publisher : McGraw-Hill Education
  • Release : 2020-04-27
  • Pages : 816
  • ISBN : 9390113059
  • Language : En, Es, Fr & De
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This text explains the meaning of variation in the context of business, with the help of real data and real business applications. It focuses not only on an in-depth explanation of the concepts but also demonstrates easily mastered software techniques using the common software available. The book is in line with the Current Statistical Practices and offers practical advice on when to use or not to use them. Salient Features: • Exclusive section for Indian Cases with questions! • New and updated Mini Cases for economics and business. • New and updated exercise data sets, web links, Big Data Sets, and Related Reading. • Updated Excel support, including screen shots, menus, and functions. • Introduction to the topic of Analytics and how it fits in with Business Statistics. • Updated exercises with emphasis on compatibility with Connect®. • Updated test bank questions matched with topics and learning objectives. • Expanded treatment of regression, including multiplicative models, interaction effects, and two sections entirely dedicated to logistic regression.

Statistical Data Analytics

Statistical Data Analytics
Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual

by Walter W. Piegorsch

  • Publisher : John Wiley & Sons
  • Release : 2015-07-01
  • Pages : 232
  • ISBN : 1119043638
  • Language : En, Es, Fr & De
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Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

Applied Data Mining

Applied Data Mining
Statistical Methods for Business and Industry

by Paolo Giudici

  • Publisher : John Wiley & Sons
  • Release : 2005-09-27
  • Pages : 376
  • ISBN : 0470871393
  • Language : En, Es, Fr & De
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Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance. This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems. Provides a solid introduction to applied data mining methods in a consistent statistical framework Includes coverage of classical, multivariate and Bayesian statistical methodology Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real life applications Features a number of detailed case studies based on applied projects within industry Incorporates discussion on software used in data mining, with particular emphasis on SAS Supported by a website featuring data sets, software and additional material Includes an extensive bibliography and pointers to further reading within the text Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.

Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
A Book

by Y. Z. Ma

  • Publisher : Springer
  • Release : 2019-07-15
  • Pages : 640
  • ISBN : 3030178609
  • Language : En, Es, Fr & De
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Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.

Statistical Modelling and Sports Business Analytics

Statistical Modelling and Sports Business Analytics
A Book

by Vanessa Ratten,Ted Hayduk

  • Publisher : Routledge
  • Release : 2020-05-11
  • Pages : 176
  • ISBN : 1000072150
  • Language : En, Es, Fr & De
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This book introduces predictive analytics in sports and discusses the relationship between analytics and algorithms and statistics. It defines sports data to be used and explains why the unique nature of sports would make analytics useful. The book also explains why the proper use of predictive analytics includes knowing what they are incapable of doing as well as the role of predictive analytics in the bigger picture of sports entrepreneurship, innovation, and technology. The book looks at the mathematical foundations that enhance technical knowledge of predictive models and illustrates through practical, insightful cases that will help to empower readers to build and deploy their own analytic methodologies. This book targets readers who already have working knowledge of location, dispersion, and distribution statistics, bivariate relationships (scatter plots and correlation coefficients), and statistical significance testing and is a reliable, well-rounded reference for furthering their knowledge of predictive analytics in sports.

Applied Predictive Analytics

Applied Predictive Analytics
Principles and Techniques for the Professional Data Analyst

by Dean Abbott

  • Publisher : John Wiley & Sons
  • Release : 2014-03-31
  • Pages : 456
  • ISBN : 111872769X
  • Language : En, Es, Fr & De
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Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Engineering and Applied Sciences Optimization

Engineering and Applied Sciences Optimization
Dedicated to the Memory of Professor M.G. Karlaftis

by Nikos D. Lagaros,Manolis Papadrakakis

  • Publisher : Springer
  • Release : 2015-05-22
  • Pages : 507
  • ISBN : 3319183206
  • Language : En, Es, Fr & De
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The chapters which appear in this volume are selected studies presented at the First International Conference on Engineering and Applied Sciences Optimization (OPT-i), Kos, Greece, 4-6 June 2014 and works written by friends, former colleagues and students of the late Professor M. G. Karlaftis; all in the area of optimization that he loved and published so much in himself. The subject areas represented here range from structural optimization, logistics, transportation, traffic and telecommunication networks to operational research, metaheuristics, multidisciplinary and multiphysics design optimization, etc. This volume is dedicated to the life and the memory of Professor Matthew G. Karlaftis, who passed away a few hours before he was to give the opening speech at OPT-i. All contributions reflect the warmth and genuine friendship which he enjoyed from his associates and show how much his scientific contribution has been appreciated. He will be greatly missed and it is hoped that this volume will be received as a suitable memorial to his life and achievements.

Statistical Methods for Materials Science

Statistical Methods for Materials Science
The Data Science of Microstructure Characterization

by Jeffrey P. Simmons,Lawrence F. Drummy,Charles A. Bouman,Marc De Graef

  • Publisher : CRC Press
  • Release : 2019-02-13
  • Pages : 514
  • ISBN : 1498738214
  • Language : En, Es, Fr & De
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Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.

Applied Survey Data Analysis

Applied Survey Data Analysis
A Book

by Steven G. Heeringa,Brady T. West,Patricia A. Berglund

  • Publisher : CRC Press
  • Release : 2017-07-12
  • Pages : 568
  • ISBN : 1498761615
  • Language : En, Es, Fr & De
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Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website.

Business Statistics in Practice: Using Data, Modeling, and Analytics

Business Statistics in Practice: Using Data, Modeling, and Analytics
A Book

by Bruce Bowerman,Richard O'Connell,Emilly Murphree

  • Publisher : McGraw-Hill Education
  • Release : 2016-01-26
  • Pages : 912
  • ISBN : 9781259549465
  • Language : En, Es, Fr & De
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Business Statistics in Practice, Eighth Edition provides a modern, practical and unique framework for teaching an introductory course in Business Statistics. The textbook employs realistic examples, continuing case studies and a business improvement theme to teach the material. The Seventh Edition features more concise and lucid explanations, an improved topic flow and a sensible use of the best and most compelling examples. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, and how they need it, so that your class time is more engaging and effective.

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
A Book

by Robert Nisbet,Gary Miner,Ken Yale

  • Publisher : Elsevier
  • Release : 2017-11-09
  • Pages : 822
  • ISBN : 0124166458
  • Language : En, Es, Fr & De
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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

New Advances in Statistics and Data Science

New Advances in Statistics and Data Science
A Book

by Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao

  • Publisher : Springer
  • Release : 2018-01-17
  • Pages : 348
  • ISBN : 3319694162
  • Language : En, Es, Fr & De
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This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.