Applied Statistical Modeling and Data Analytics Book

Applied Statistical Modeling and Data Analytics

  • Author : Srikanta Mishra
  • Publisher : Elsevier
  • Release Date : 2017-10-27
  • Genre: Science
  • Pages : 250
  • ISBN 10 : 9780128032800

Applied Statistical Modeling and Data Analytics Excerpt :

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

Statistical Modeling and Analysis for Complex Data Problems Book

Statistical Modeling and Analysis for Complex Data Problems

  • Author : Pierre Duchesne
  • Publisher : Springer Science & Business Media
  • Release Date : 2005-12-05
  • Genre: Mathematics
  • Pages : 324
  • ISBN 10 : 9780387245553

Statistical Modeling and Analysis for Complex Data Problems Excerpt :

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 Modelling and Sports Business Analytics Book

Statistical Modelling and Sports Business Analytics

  • Author : Vanessa Ratten
  • Publisher : Routledge
  • Release Date : 2020-05-11
  • Genre: Business & Economics
  • Pages : 175
  • ISBN 10 : 9781000072150

Statistical Modelling and Sports Business Analytics Excerpt :

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.

Statistical Learning and Modeling in Data Analysis Book

Statistical Learning and Modeling in Data Analysis

  • Author : Simona Balzano
  • Publisher : Springer Nature
  • Release Date : 2021-07-13
  • Genre: Mathematics
  • Pages : 182
  • ISBN 10 : 9783030699444

Statistical Learning and Modeling in Data Analysis Excerpt :

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 presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11–13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG’s goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.

Data Analysis Using Regression and Multilevel Hierarchical Models Book
Score: 4.5
From 4 Ratings

Data Analysis Using Regression and Multilevel Hierarchical Models

  • Author : Andrew Gelman
  • Publisher : Cambridge University Press
  • Release Date : 2007
  • Genre: Mathematics
  • Pages : 654
  • ISBN 10 : 052168689X

Data Analysis Using Regression and Multilevel Hierarchical Models Excerpt :

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

Statistical Data Analysis Using SAS Book

Statistical Data Analysis Using SAS

  • Author : Mervyn G. Marasinghe
  • Publisher : Springer
  • Release Date : 2018-04-12
  • Genre: Computers
  • Pages : 679
  • ISBN 10 : 9783319692395

Statistical Data Analysis Using SAS Excerpt :

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 effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate

Applied Data Analysis and Modeling for Energy Engineers and Scientists Book

Applied Data Analysis and Modeling for Energy Engineers and Scientists

  • Author : T. Agami Reddy
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-08-09
  • Genre: Technology & Engineering
  • Pages : 430
  • ISBN 10 : 1441996133

Applied Data Analysis and Modeling for Energy Engineers and Scientists Excerpt :

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.

Spatial Regression Models Book

Spatial Regression Models

  • Author : Michael D. Ward
  • Publisher : SAGE Publications
  • Release Date : 2018-04-10
  • Genre: Social Science
  • Pages : 128
  • ISBN 10 : 9781544328812

Spatial Regression Models Excerpt :

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 authors illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models. Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.

Multivariate Statistical Modeling and Data Analysis Book

Multivariate Statistical Modeling and Data Analysis

  • Author : H. Bozdogan
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • Genre: Mathematics
  • Pages : 189
  • ISBN 10 : 9789400939776

Multivariate Statistical Modeling and Data Analysis Excerpt :

This volume contains the Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis held at the 64th Annual Heeting of the Virginia Academy of Sciences (VAS)--American Statistical Association's Vir ginia Chapter at James Madison University in Harrisonburg. Virginia during Hay 15-16. 1986. This symposium was sponsored by financial support from the Center for Advanced Studies at the University of Virginia to promote new and modern information-theoretic statist ical modeling procedures and to blend these new techniques within the classical theory. Multivariate statistical analysis has come a long way and currently it is in an evolutionary stage in the era of high-speed computation and computer technology. The Advanced Symposium was the first to address the new innovative approaches in multi variate analysis to develop modern analytical and yet practical procedures to meet the needs of researchers and the societal need of statistics. vii viii PREFACE Papers presented at the Symposium by e1l11lJinent researchers in the field were geared not Just for specialists in statistics, but an attempt has been made to achieve a well balanced and uniform coverage of different areas in multi variate modeling and data analysis. The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.

New Perspectives in Statistical Modeling and Data Analysis Book

New Perspectives in Statistical Modeling and Data Analysis

  • Author : Salvatore Ingrassia
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-06-29
  • Genre: Mathematics
  • Pages : 587
  • ISBN 10 : 9783642113635

New Perspectives in Statistical Modeling and Data Analysis Excerpt :

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 Society, held at the University of Catania, Italy.

Statistical Foundations  Reasoning and Inference Book

Statistical Foundations Reasoning and Inference

  • Author : Göran Kauermann
  • Publisher : Springer Nature
  • Release Date : 2021-09-30
  • Genre: Mathematics
  • Pages : 361
  • ISBN 10 : 9783030698270

Statistical Foundations Reasoning and Inference Excerpt :

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

Empirical Modeling and Data Analysis for Engineers and Applied Scientists Book

Empirical Modeling and Data Analysis for Engineers and Applied Scientists

  • Author : Scott A. Pardo
  • Publisher : Springer
  • Release Date : 2016-07-19
  • Genre: Mathematics
  • Pages : 247
  • ISBN 10 : 9783319327686

Empirical Modeling and Data Analysis for Engineers and Applied Scientists Excerpt :

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, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a

Applied Predictive Modeling Book
Score: 5
From 1 Ratings

Applied Predictive Modeling

  • Author : Max Kuhn
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-05-17
  • Genre: Medical
  • Pages : 600
  • ISBN 10 : 9781461468493

Applied Predictive Modeling Excerpt :

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.

Real Estate Analysis in the Information Age Book

Real Estate Analysis in the Information Age

  • Author : Kimberly Winson-Geideman
  • Publisher : Routledge
  • Release Date : 2017-11-09
  • Genre: Business & Economics
  • Pages : 296
  • ISBN 10 : 9781315311111

Real Estate Analysis in the Information Age Excerpt :

The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate.

Recent Studies on Risk Analysis and Statistical Modeling Book

Recent Studies on Risk Analysis and Statistical Modeling

  • Author : Teresa A. Oliveira
  • Publisher : Springer
  • Release Date : 2018-08-22
  • Genre: Mathematics
  • Pages : 375
  • ISBN 10 : 9783319766058

Recent Studies on Risk Analysis and Statistical Modeling Excerpt :

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.