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

Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences�i�A�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 s...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Mishra, Srikanta, 1958- (Autor), Datta-Gupta, Akhil, 1960- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA : Elsevier, [2018]
Edición:First edition.
Temas:
Petroleum > Geology > Statistical methods.
Petroleum > Geology > Data processing.
P�etrole > G�eologie > M�ethodes statistiques.
P�etrole > G�eologie > Informatique.
TECHNOLOGY & ENGINEERING > Mining.
Petroleum > Geology > Data processing
Petroleum > Geology > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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