Data Analysis for the Geosciences

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  • Data Analysis for the Geosciences Book Detail

  • Author : Michael W. Liemohn
  • Release Date : 2023-10-10
  • Publisher : John Wiley & Sons
  • Genre : Science
  • Pages : 452
  • ISBN 13 : 1119747899
  • File Size : 48,48 MB

Data Analysis for the Geosciences by Michael W. Liemohn PDF Summary

Book 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.

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Data Analysis for the Geosciences

Data Analysis for the Geosciences

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

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It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more diffic