Data Analysis and Statistics for Geography, Environmental Science, and Engineering

Miguel F. Acevedo

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December 7, 2012 by CRC Press
Textbook - 557 Pages - 320 B/W Illustrations
ISBN 9781439885017 - CAT# K13985

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Features

  • Provides an integrated practical guide to data analysis and statistics using free open-source R software
  • Presents a broad focus geared towards sustainability science and engineering
  • Reviews the necessary mathematics and theoretical fundamentals
  • Includes practical exercises using free open-source R software and written with a tutorial style
  • Helps readers acquire skills to implement and conduct analysis and analyze results
  • Includes more than 300 figures to aid in comprehension

Pedagogical Features

  • Solved examples
  • Homework problems
  • Guided computer exercises

A solutions manual and PowerPoint® slides with figures and equations are available with qualifying course adoption

Summary

Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustainability, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applicable across a variety of science and engineering disciplines.

Learn How to Use a Variety of Data Analysis and Statistics Methods

Based on the author’s many years of teaching graduate and undergraduate students, this textbook emphasizes hands-on learning. Organized into two parts, it allows greater flexibility using the material in various countries and types of curricula. The first part covers probability, random variables and inferential statistics, applications of regression, time series analysis, and analysis of spatial point patterns. The second part uses matrix algebra to address multidimensional problems. After a review of matrices, it delves into multiple regression, dependent random processes and autoregressive time series, spatial analysis using geostatistics and spatial regression, discriminant analysis, and a variety of multivariate analyses based on eigenvector methods.

Build from Fundamental Concepts to Effective Problem Solving

Each chapter starts with conceptual and theoretical material to give a firm foundation in how the methods work. Examples and exercises illustrate the applications and demonstrate how to go from concepts to problem solving. Hands-on computer sessions allow students to grasp the practical implications and learn by doing. Throughout, the computer examples and exercises use seeg and RcmdrPlugin.seeg, open-source R packages developed by the author, which help students acquire the skills to implement and conduct analysis and to analyze the results.

This self-contained book offers a unified presentation of data analysis methods for more effective problem solving. With clear, easy-to-follow explanations, the book helps students to develop a solid understanding of basic statistical analysis and prepares them for learning the more advanced and specialized methods they will need in their work.