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Record Nr. |
UNINA9910460107103321 |
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Autore |
Acevedo Miguel F. |
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Titolo |
Data analysis and statistics for geography, environmental science, and engineering / / by Miguel F. Acevedo |
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Pubbl/distr/stampa |
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Boca Raton, FL : , : CRC Press, an imprint of Taylor and Francis, , 2012 |
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ISBN |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (549 p.) |
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Disciplina |
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Soggetti |
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Geography - Data processing |
Geography - Statistical methods |
Environmental sciences - Data processing |
Environmental sciences - Statistical methods |
Engineering - Data processing |
Engineering - Statistical methods |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Front Cover; Contents; Preface; Acknowledgments; Author; Chapter 1 - Introduction; Chapter 2 - Probability Theory; Chapter 3 - Random Variables, Distributions, Moments, and Statistics; Chapter 4 - Exploratory Analysis and Introduction to Inferential Statistics; Chapter 5 - More on Inferential Statistics: Goodness of Fit, Contingency Analysis, and Analysis of Variance; Chapter 6 - Regression; Chapter 7 - Stochastic or Random Processes and Time Series; Chapter 8 - Spatial Point Patterns; Chapter 9 - Matrices and Linear Algebra; Chapter 10 - Multivariate Models |
Chapter 11 - Dependent Stochastic Processes and Time SeriesChapter 12 - Geostatistics: Kriging; Chapter 13 - Spatial Auto-Correlation and Auto-Regression; Chapter 14 - Multivariate Analysis I: Reducing Dimensionality; Chapter 15 - Multivariate Analysis II: Identifying and Developing Relationships among Observations and Variables; Bibliography; Back Cover |
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Sommario/riassunto |
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Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental |
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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. |
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