Environmental data analysis with MatLab [recurso electrónico] / William Menke, Joshua Menke.

Por: Menke, WilliamColaborador(es): Menke, Joshua E. (Joshua Ephraim), 1976-Tipo de material: TextoTextoDetalles de publicación: Burlington : Elsevier, c2012Descripción: 1 online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9780123918864 (electronic bk.); 0123918863 (electronic bk.); 9780123918871; 0123918871; 1283249928; 9781283249928Tema(s): Environmental sciences -- Mathematical models | Environmental sciences -- Data processing | MATLAB | MATLAB | Environmental sciences -- Data processing | Environmental sciences -- Mathematical models | Environmental sciences -- Data processing | Environmental sciences -- Mathematical models | MATLAB | Environmental StudiesGénero/Forma: Electronic books.Formatos físicos adicionales: Print version:: Environmental data analysis with MatLab.Clasificación CDD: 363.7001/5118 Clasificación LoC:GE45.M37 | M46 2012ebRecursos en línea: Libro electrónico ScienceDirectTexto
Contenidos:
1. Data Analysis with MatLab -- 2. A First Look at Data -- 3. Probability and What has to do with Data. -- 4. The Power of Linear Models -- 5. Quantifying Preconceptions -- 6. Detecting Periodicities -- 7. The Past Influences the Present -- 8. Patterns Suggested By Data -- 9. Detecting Correlations Among the Data -- 10. Filling in Missing Data -- 11. Are my Results Significant? -- 12. Notes.
Resumen: Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. Well written and outlines a clear learning path for researchers and students Uses real world environmental examples and case studies MatLab software for application in a readily-available software environment Homework problems help user follow up upon case studies with homework that expands them.
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Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos GE45 .M37 M46 2012 EB (Browse shelf(Abre debajo)) 1 No para préstamo 380661-2001

Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. Well written and outlines a clear learning path for researchers and students Uses real world environmental examples and case studies MatLab software for application in a readily-available software environment Homework problems help user follow up upon case studies with homework that expands them.

1. Data Analysis with MatLab -- 2. A First Look at Data -- 3. Probability and What has to do with Data. -- 4. The Power of Linear Models -- 5. Quantifying Preconceptions -- 6. Detecting Periodicities -- 7. The Past Influences the Present -- 8. Patterns Suggested By Data -- 9. Detecting Correlations Among the Data -- 10. Filling in Missing Data -- 11. Are my Results Significant? -- 12. Notes.

Includes bibliographical references and index.

Description based on print version record.

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