Data Analysis for the Geosciences
eBook - PDF

Data Analysis for the Geosciences

Essentials of Uncertainty, Comparison, and Visualization

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Data Analysis for the Geosciences

Essentials of Uncertainty, Comparison, and Visualization

About this book

An initial course in scientific data analysis and hypothesis testing designed for students in all science, technology, engineering, and mathematics disciplines

Data Analysis for the Geosciences: Essentials of Uncertainty, Comparison, and Visualization is a textbook for upper-level undergraduate STEM students, designed to be their statistics course in a degree program.

This volume provides a comprehensive introduction to data analysis, visualization, and data-model comparisons and metrics, within the framework of the uncertainty around the values. It offers a learning experience based on real data from the Earth, ocean, atmospheric, space, and planetary sciences.

About this volume:

  • Serves as an initial course in scientific data analysis and hypothesis testing
  • Focuses on the methods of data processing
  • Introduces a wide range of analysis techniques
  • Describes the many ways to compare data with models
  • Centers on applications rather than derivations
  • Explains how to select appropriate statistics for meaningful decisions
  • Explores the importance of the concept of uncertainty
  • Uses examples from real geoscience observations
  • Homework problems at the end of chapters

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

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Yes, you can access Data Analysis for the Geosciences by Michael W. Liemohn in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Geology & Earth Sciences. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Contents
  6. Preface
  7. Acknowledgments
  8. About the Companion Website
  9. Chapter 1 Assessment and Uncertainty: Examples and Introductory Concepts
  10. Chapter 2 Plotting Data: Visualizing Sets of Numbers
  11. Chapter 3 Uncertainty Analysis: Techniques for Propagating Uncertainty
  12. Chapter 4 Centroids and Spreads: Analyzing a Set of Numbers
  13. Chapter 5 Assessing Normality: Tests for Assessing the Gaussian Nature of a Distribution
  14. Chapter 6 Correlating Two Data Sets: Analyzing Two Sets of Numbers Together
  15. Chapter 7 Curve Fitting: Fitting a Line between Two Sets of Numbers
  16. Chapter 8 Data-Model Comparison Basics: Philosophies of Calculating and Categorizing Metrics
  17. Chapter 9 Fit Performance Metrics: Data-Model Comparisons Based on Exact Observed and Modeled Values
  18. Chapter 10 Event Detection Metrics: Comparing Observed and Modeled Number Sets When Only Event Status Matters
  19. Chapter 11 Sliding Thresholds: Event Detection Metrics with a Variable Event Identification
  20. Chapter 12 Applications of Metrics and Uncertainty: Final Advice and Introductions to Advanced Topics
  21. Index
  22. EULA