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Data Analysis and Statistics for Geography, Environmental Science, and Engineering (Hardcover)
Loot Price: R3,087
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Data Analysis and Statistics for Geography, Environmental Science, and Engineering (Hardcover)
Expected to ship within 12 - 17 working days
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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.
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