Applied Statistical Modeling and Data Analytics

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  • Publisher : Elsevier
  • Release : 27 October 2017
  • ISBN : 9780128032800
  • Page : 250 pages
  • Rating : 4.5/5 from 103 voters

Applied Statistical Modeling and Data Analytics Book PDF summary

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

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Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics
  • Author : Srikanta Mishra,Akhil Datta-Gupta
  • Publisher : Elsevier
  • Release Date : 2017-10-27
  • ISBN : 9780128032800
DOWNLOAD BOOKApplied Statistical Modeling and Data Analytics

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

Statistical Modeling and Analysis for Complex Data Problems

Statistical Modeling and Analysis for Complex Data Problems
  • Author : Pierre Duchesne,Bruno Rémillard
  • Publisher : Springer Science & Business Media
  • Release Date : 2005-12-05
  • ISBN : 9780387245553
DOWNLOAD BOOKStatistical Modeling and Analysis for Complex Data Problems

This book reviews some of today’s more complex problems, and reflects some of the important research directions in the field. Twenty-nine authors – largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes – present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.

Statistical Learning and Modeling in Data Analysis

Statistical Learning and Modeling in Data Analysis
  • Author : Simona Balzano,Giovanni C. Porzio,Renato Salvatore,Domenico Vistocco,Maurizio Vichi
  • Publisher : Springer
  • Release Date : 2021-07-14
  • ISBN : 3030699439
DOWNLOAD BOOKStatistical Learning and Modeling in Data Analysis

The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk. The book gathers selected and peer-reviewed contributions

Statistical Modelling and Sports Business Analytics

Statistical Modelling and Sports Business Analytics
  • Author : Vanessa Ratten,Ted Hayduk
  • Publisher : Routledge
  • Release Date : 2020-05-11
  • ISBN : 9781000072150
DOWNLOAD BOOKStatistical Modelling and Sports Business Analytics

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

Applied Data Analysis and Modeling for Energy Engineers and Scientists

Applied Data Analysis and Modeling for Energy Engineers and Scientists
  • Author : T. Agami Reddy
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-08-09
  • ISBN : 1441996133
DOWNLOAD BOOKApplied Data Analysis and Modeling for Energy Engineers and Scientists

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

Empirical Modeling and Data Analysis for Engineers and Applied Scientists

Empirical Modeling and Data Analysis for Engineers and Applied Scientists
  • Author : Scott A. Pardo
  • Publisher : Springer
  • Release Date : 2016-07-19
  • ISBN : 9783319327686
DOWNLOAD BOOKEmpirical Modeling and Data Analysis for Engineers and Applied Scientists

This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics,

Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models
  • Author : Andrew Gelman,Professor in the Department of Statistics Andrew Gelman,Jennifer Hill
  • Publisher : Cambridge University Press
  • Release Date : 2007
  • ISBN : 052168689X
DOWNLOAD BOOKData Analysis Using Regression and Multilevel/Hierarchical Models

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Applied Predictive Modeling

Applied Predictive Modeling
  • Author : Max Kuhn,Kjell Johnson
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-05-17
  • ISBN : 9781461468493
DOWNLOAD BOOKApplied Predictive Modeling

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

Discrete Data Analysis with R

Discrete Data Analysis with R
  • Author : Michael Friendly,David Meyer
  • Publisher : CRC Press
  • Release Date : 2015-12-16
  • ISBN : 9781498725866
DOWNLOAD BOOKDiscrete Data Analysis with R

An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical meth

Statistical Data Analysis Using SAS

Statistical Data Analysis Using SAS
  • Author : Mervyn G. Marasinghe,Kenneth J. Koehler
  • Publisher : Springer
  • Release Date : 2018-04-12
  • ISBN : 9783319692395
DOWNLOAD BOOKStatistical Data Analysis Using SAS

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed

Spatial Regression Models

Spatial Regression Models
  • Author : Michael D. Ward,Kristian Skrede Gleditsch
  • Publisher : SAGE Publications
  • Release Date : 2018-04-10
  • ISBN : 9781544328812
DOWNLOAD BOOKSpatial Regression Models

Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social science examples based on real data, the

New Perspectives in Statistical Modeling and Data Analysis

New Perspectives in Statistical Modeling and Data Analysis
  • Author : Salvatore Ingrassia,Roberto Rocci,Maurizio Vichi
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-06-29
  • ISBN : 9783642113635
DOWNLOAD BOOKNew Perspectives in Statistical Modeling and Data Analysis

This volume provides recent research results in data analysis, classification and multivariate statistics and highlights perspectives for new scientific developments within these areas. Particular attention is devoted to methodological issues in clustering, statistical modeling and data mining. The volume also contains significant contributions to a wide range of applications such as finance, marketing, and social sciences. The papers in this volume were first presented at the 7th Conference of the Classification and Data Analysis Group (ClaDAG) of the Italian Statistical

Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics

Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
  • Author : Patil, Bhushan,Vohra, Manisha
  • Publisher : IGI Global
  • Release Date : 2020-10-23
  • ISBN : 9781799830542
DOWNLOAD BOOKHandbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics

Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data

Statistical Modeling in Biomedical Research

Statistical Modeling in Biomedical Research
  • Author : Yichuan Zhao,Ding-Geng (Din) Chen
  • Publisher : Springer Nature
  • Release Date : 2020-03-19
  • ISBN : 9783030334161
DOWNLOAD BOOKStatistical Modeling in Biomedical Research

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate

Learn R for Applied Statistics

Learn R for Applied Statistics
  • Author : Eric Goh Ming Hui
  • Publisher : Apress
  • Release Date : 2018-11-30
  • ISBN : 9781484242001
DOWNLOAD BOOKLearn R for Applied Statistics

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