Miguel F. Acevedo

Data Analysis and Statistics for Geography, Environmental Science, and Engineering (eBook, PDF)

• Format: PDF

Produktbeschreibung

This practical, classroom-tested textbook helps readers learn quantitative methodology, including how to implement advanced analysis methods using an open-source software platform. Based on the author's many years of teaching undergraduate and graduate students in several countries, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applied to a variety of geographical and environmental models. Theory is accompanied by practical hands-on computer exercises, progressing from easy to difficult. The text also presents a review of mathematical methods, making the book self-contained.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 557
• Erscheinungstermin: 7. Dezember 2012
• Englisch
• ISBN-13: 9781466592216
• Artikelnr.: 57101749

Autorenporträt

Miguel F. Acevedo has 38 years of academic experience, the last 20 of these as faculty member of the University of North Texas (UNT). His career has been interdisciplinary, especially at the interface of science and engineering. He obtained his Ph.D. in biophysics from the University of California Berkeley and master's degrees in electrical engineering and computer science from Berkeley and the University of Texas at Austin, respectively. Prior to UNT, he was at the Universidad de Los Andes in Merida, Venezuela, where he taught for 18 years. He has served on the Science Advisory Board of the U.S. Environmental Protection Agency and on many review panels of the U.S. National Science Foundation. He has received numerous research grants and written many journal articles, book chapters, and proceedings articles. UNT has recognized him with the Regent's Professor rank, the Citation for Distinguished Service to International Education, and the Regent's Faculty Lectureship. For more information, see Dr. Acevedo's page at UNT.

Inhaltsangabe

Part I Introduction to Probability, Statistics, Time Series, and Spatial
Analysis: Introduction. Probability Theory. Random Variables,
Distributions, Moments, and Statistics. Exploratory Analysis and
Introduction to Inferential Statistics. More on Inferential Statistics:
Goodness of Fit, Contingency Analysis, and Analysis of Variance.
Regression. Stochastic or Random Processes and Time Series. Spatial Point
Patterns. Part II Matrices, Tempral and Spatial Autoregressive Processes,
and Multivariate Analysis: Matrices and Linear Algebra. Multivariate
Models. Dependent Stochastic Processes and Time Series. Geostatistics:
Kriging. Spatial Auto-Correlation and Auto-Regression. Multivariate
Analysis I: Reducing Dimensionality. Multivariate Analysis II: Identifying
and Developing Relationships among Observations and Variables.
Bibliography. Index.