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Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics
  • Author : Srikanta Mishra
  • Publsiher : Elsevier
  • Release : 27 October 2017
  • ISBN : 0128032804
  • Pages : 250 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKApplied Statistical Modeling and Data Analytics

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


Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics
  • Author : Srikanta Mishra,Akhil Datta-Gupta
  • Publisher : Elsevier
  • Release : 27 October 2017
GET THIS 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



Applied Predictive Modeling

Applied Predictive Modeling
  • Author : Max Kuhn,Kjell Johnson
  • Publisher : Springer Science & Business Media
  • Release : 17 May 2013
GET THIS 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


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 : 09 August 2011
GET THIS 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


Advances in Statistical Models for Data Analysis

Advances in Statistical Models for Data Analysis
  • Author : Isabella Morlini,Tommaso Minerva,Maurizio Vichi
  • Publisher : Springer
  • Release : 04 September 2015
GET THIS BOOKAdvances in Statistical Models for Data Analysis

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


Recent Studies on Risk Analysis and Statistical Modeling

Recent Studies on Risk Analysis and Statistical Modeling
  • Author : Teresa A. Oliveira,Christos P. Kitsos,Amílcar Oliveira,Luís Grilo
  • Publisher : Springer
  • Release : 22 August 2018
GET THIS BOOKRecent Studies on Risk Analysis and Statistical Modeling

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


Applied Predictive Analytics

Applied Predictive Analytics
  • Author : Dean Abbott
  • Publisher : John Wiley & Sons
  • Release : 31 March 2014
GET THIS BOOKApplied Predictive Analytics

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


Applied Data Mining

Applied Data Mining
  • Author : Paolo Giudici
  • Publisher : John Wiley & Sons
  • Release : 27 September 2005
GET THIS BOOKApplied Data Mining

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


Learn R for Applied Statistics

Learn R for Applied Statistics
  • Author : Eric Goh Ming Hui
  • Publisher : Apress
  • Release : 30 November 2018
GET THIS 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


Applied Analytics through Case Studies Using SAS and R

Applied Analytics through Case Studies Using SAS and R
  • Author : Deepti Gupta
  • Publisher : Apress
  • Release : 03 August 2018
GET THIS BOOKApplied Analytics through Case Studies Using SAS and R

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building


Stochastic Models, Statistics and Their Applications

Stochastic Models, Statistics and Their Applications
  • Author : Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin
  • Publisher : Springer Nature
  • Release : 15 October 2019
GET THIS BOOKStochastic Models, Statistics and Their Applications

This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory,


Applied Statistics in Business and Economics | Sixth Edition | SIE

Applied Statistics in Business and Economics | Sixth Edition | SIE
  • Author : David P. Doane,Lori E. Seward,Shovan Chowdhury
  • Publisher : McGraw-Hill Education
  • Release : 27 April 2020
GET THIS BOOKApplied Statistics in Business and Economics | Sixth Edition | SIE

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


Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
  • Author : Robert Nisbet,Gary Miner,Ken Yale
  • Publisher : Elsevier
  • Release : 09 November 2017
GET THIS BOOKHandbook of Statistical Analysis and Data Mining Applications

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


Applied Data Science

Applied Data Science
  • Author : Martin Braschler,Thilo Stadelmann,Kurt Stockinger
  • Publisher : Springer
  • Release : 13 June 2019
GET THIS BOOKApplied Data Science

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and


Modeling Techniques in Predictive Analytics

Modeling Techniques in Predictive Analytics
  • Author : Thomas W. Miller
  • Publisher : FT Press
  • Release : 29 September 2014
GET THIS BOOKModeling Techniques in Predictive Analytics

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer,