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Data Analysis for the Geosciences
◆JpGU2025 セール開催中！：2025年6月30日(月)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Essentials of Uncertainty, Comparison, and Visualization Series: AGU Advanced Textbooks Author: Liemohn, Michael W. (University of Michigan, USA) Publisher: WILEY ISBN: 9781119747871 Cover: PAPERBACK Date: 2023年11月 DESCRIPTION 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. TABLE OF CONTENTS 1 Assessment and Uncertainty: Examples and Introductory Concepts 1 2 Plotting Data: Visualizing Sets of Numbers 27 3 Uncertainty Analysis: Techniques for Propagating Uncertainty 69 4 Centroids and Spreads: Analyzing a Set of Numbers 95 5 Assessing Normality: Tests for Assessing the Gaussian Nature of a Distribution 123 6 Correlating Two Data Sets: Analyzing Two Sets of Numbers Together 153 7 Curve Fitting: Fitting a Line between Two Sets of Numbers 181 8 Data- Model Comparison Basics: Philosophies of Calculating and Categorizing Metrics 213 9 Fit Performance Metrics: Data- Model Comparisons Based on Exact Observed and Modeled Values 243 10 Event Detection Metrics: Comparing Observed and Modeled Number Sets When Only Event Status Matters 295 11 Sliding Thresholds: Event Detection Metrics with a Variable Event Identification 333 12 Applications of Metrics and Uncertainty: Final Advice and Introductions to Advanced Topics 367
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