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...

Full description

Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Mishra, Srikanta, 1958- (Author), Datta-Gupta, Akhil, 1960- (Author)
Format: Electronic eBook
Language:Inglés
Published: Cambridge, MA : Elsevier, [2018]
Edition:First edition.
Subjects:
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
Online Access:Texto completo
Table of Contents:
  • 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

Similar Items