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Applied statistical modeling and dat...

Mishra, Srikanta, (1958-)

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Applied statistical modeling and data analytics = a practical guide for the petroleum geosciences /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied statistical modeling and data analytics/ Srikanta Mishra, Akhil Datta-Gupta.
其他題名:	a practical guide for the petroleum geosciences /
作者:	Mishra, Srikanta,
其他作者:	Datta-Gupta, Akhil,
出版者:	Cambridge, MA :Elsevier, : 2018.,
面頁冊數:	1 online resource.
內容註:	Front Cover -- Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1: Basic Concepts -- 1.1. Background and Scope -- 1.1.1. What Is Statistics? -- 1.1.2. What Is Big Data Analytics? -- 1.1.3. Data Analysis Cycle -- 1.1.4. Some Applications in the Petroleum Geosciences -- 1.2. Data, Statistics, and Probability -- 1.2.1. Outcomes and Events -- 1.2.2. Probability -- 1.2.3. Conditional Probability and Bayes Rule -- 1.3. Random Variables
內容註:	1.3.1. Discrete Case1.3.2. Continuous Case -- 1.3.3. Indicator Transform -- 1.4. Summary -- Exercises -- References -- Chapter 2: Exploratory Data Analysis -- 2.1. Univariate Data -- 2.1.1. Measures of Center -- 2.1.2. Measures of Spread -- 2.1.3. Measures of Asymmetry -- 2.1.4. Graphing Univariate Data -- 2.2. Bivariate Data -- 2.2.1. Covariance -- 2.2.2. Correlation and Rank Correlation -- 2.2.3. Graphing Bivariate Data -- 2.3. Multivariate Data -- 2.4. Summary -- Exercises -- References -- Chapter 3: Distributions and Models Thereof
內容註:	3.1. Empirical Distributions3.1.1. Histogram -- 3.1.2. Quantile Plot -- 3.2. Parametric Models -- 3.2.1. Uniform Distribution -- 3.2.2. Triangular Distribution -- 3.2.3. Normal Distribution -- 3.2.4. Lognormal Distribution -- 3.2.5. Poisson Distribution -- 3.2.6. Exponential Distribution -- 3.2.7. Binomial Distribution -- 3.2.8. Weibull Distribution -- 3.2.9. Beta Distribution -- 3.3. Working With Normal and Log-Normal Distributions -- 3.3.1. Normal Distribution -- 3.3.2. Normal Score Transformation -- 3.3.3. Log-Normal Distribution
內容註:	3.4. Fitting Distributions to Data3.4.1. Probability Plots -- 3.4.2. Parameter Estimation Techniques -- Linear Regression Analysis -- Method of Moments -- Nonlinear Least-Squares Analysis -- 3.5. Other Properties of Distributions and Their Evaluation -- 3.5.1. Central Limit Theorem and Confidence Limits -- 3.5.2. Bootstrap Sampling -- 3.5.3. Comparing Two Distributions -- Q-Q Plot -- Testing for Difference in Mean -- Testing for Difference in Distributions -- Other Methods for Comparing Distributions -- 3.6. Summary -- Exercises -- References
內容註:	Chapter 4: Regression Modeling and Analysis4.1. Introduction -- 4.2. Simple Linear Regression -- 4.2.1. Formulating and Solving the Linear Regression Problem -- 4.2.2. Evaluating the Linear Regression Model -- 4.2.3. Properties of the Regression Parameters and Confidence Limits -- 4.2.4. Estimating Confidence Intervals for the Mean Response and Forecast -- 4.2.5. An Illustrative Example of Linear Regression Modeling and Analysis -- 4.3. Multiple Regression -- 4.3.1. Formulating and Solving the Multiple Regression Model
標題:	Petroleum - Geology -
電子資源:	https://www.sciencedirect.com/science/book/9780128032794
ISBN:	9780128032800 (electronic bk.)

Applied statistical modeling and data analytics = a practical guide for the petroleum geosciences /
Mishra, Srikanta,1958-

Applied statistical modeling and data analyticsa practical guide for the petroleum geosciences /[electronic resource] :Srikanta Mishra, Akhil Datta-Gupta. - First edition. - Cambridge, MA :Elsevier,2018. - 1 online resource.

Includes bibliographical references and index.

Front Cover -- Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1: Basic Concepts -- 1.1. Background and Scope -- 1.1.1. What Is Statistics? -- 1.1.2. What Is Big Data Analytics? -- 1.1.3. Data Analysis Cycle -- 1.1.4. Some Applications in the Petroleum Geosciences -- 1.2. Data, Statistics, and Probability -- 1.2.1. Outcomes and Events -- 1.2.2. Probability -- 1.2.3. Conditional Probability and Bayes Rule -- 1.3. Random Variables

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

ISBN: 9780128032800 (electronic bk.)Subjects--Topical Terms:

596827
Petroleum
--GeologyIndex Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: TN871

Dewey Class. No.: 622/.3382

Applied statistical modeling and data analytics = a practical guide for the petroleum geosciences /
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505 0 $a Front Cover -- Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1: Basic Concepts -- 1.1. Background and Scope -- 1.1.1. What Is Statistics? -- 1.1.2. What Is Big Data Analytics? -- 1.1.3. Data Analysis Cycle -- 1.1.4. Some Applications in the Petroleum Geosciences -- 1.2. Data, Statistics, and Probability -- 1.2.1. Outcomes and Events -- 1.2.2. Probability -- 1.2.3. Conditional Probability and Bayes Rule -- 1.3. Random Variables
505 8 $a 1.3.1. Discrete Case1.3.2. Continuous Case -- 1.3.3. Indicator Transform -- 1.4. Summary -- Exercises -- References -- Chapter 2: Exploratory Data Analysis -- 2.1. Univariate Data -- 2.1.1. Measures of Center -- 2.1.2. Measures of Spread -- 2.1.3. Measures of Asymmetry -- 2.1.4. Graphing Univariate Data -- 2.2. Bivariate Data -- 2.2.1. Covariance -- 2.2.2. Correlation and Rank Correlation -- 2.2.3. Graphing Bivariate Data -- 2.3. Multivariate Data -- 2.4. Summary -- Exercises -- References -- Chapter 3: Distributions and Models Thereof
505 8 $a 3.1. Empirical Distributions3.1.1. Histogram -- 3.1.2. Quantile Plot -- 3.2. Parametric Models -- 3.2.1. Uniform Distribution -- 3.2.2. Triangular Distribution -- 3.2.3. Normal Distribution -- 3.2.4. Lognormal Distribution -- 3.2.5. Poisson Distribution -- 3.2.6. Exponential Distribution -- 3.2.7. Binomial Distribution -- 3.2.8. Weibull Distribution -- 3.2.9. Beta Distribution -- 3.3. Working With Normal and Log-Normal Distributions -- 3.3.1. Normal Distribution -- 3.3.2. Normal Score Transformation -- 3.3.3. Log-Normal Distribution
505 8 $a 3.4. Fitting Distributions to Data3.4.1. Probability Plots -- 3.4.2. Parameter Estimation Techniques -- Linear Regression Analysis -- Method of Moments -- Nonlinear Least-Squares Analysis -- 3.5. Other Properties of Distributions and Their Evaluation -- 3.5.1. Central Limit Theorem and Confidence Limits -- 3.5.2. Bootstrap Sampling -- 3.5.3. Comparing Two Distributions -- Q-Q Plot -- Testing for Difference in Mean -- Testing for Difference in Distributions -- Other Methods for Comparing Distributions -- 3.6. Summary -- Exercises -- References
505 8 $a Chapter 4: Regression Modeling and Analysis4.1. Introduction -- 4.2. Simple Linear Regression -- 4.2.1. Formulating and Solving the Linear Regression Problem -- 4.2.2. Evaluating the Linear Regression Model -- 4.2.3. Properties of the Regression Parameters and Confidence Limits -- 4.2.4. Estimating Confidence Intervals for the Mean Response and Forecast -- 4.2.5. An Illustrative Example of Linear Regression Modeling and Analysis -- 4.3. Multiple Regression -- 4.3.1. Formulating and Solving the Multiple Regression Model
520 $a Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum GeosciencesïÅưprovides 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
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