Vista normal Vista MARC

Statistics of earth science data: their distribution in time, space, and orientation [Libro electrónico] / autor: Graham Borradaile

Por: Borradaile, Graham [autor/a].
Tipo de material: Libro en línea Libro en línea Editor: New York, New York, United States: Springer-Verlag Berlin Heidelberg, c2003Descripción: xxvii, 351 páginas : ilustraciones mapas ; 28 centímetros.ISBN: 3540436030; 9783642078156 (Print); 9783662052235 (Online).Tema(s): Earth sciences -- Statistical methodsNota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso Nota de bibliografía: Incluye bibliografía e índice: páginas 337-343 Número de sistema: 56514Contenidos:Mostrar Resumen:
Inglés

The Goals of Data Collection and Its Statistical Treatment in the Earth Sciences The earth sciences are characterised by loose and complex relationships between variables, and the necessity to understand the geographical dis­ tribution of observations as well as their frequency distribution. Our fre­ quency distributions and the looseness of relationships reflect the com­ plexity and intrinsic natural variation in nature, more than measurement error. Furthermore, earth scientists cannot design experiments according to statistical recommendation because the availability and complexity of data are beyond our control. Usually, the system we are studying cannot be isolated into discrete independent variables. These factors influence the first steps of research, how and where to collect specimens or observations. Some issues are particularly troublesome and common in earth science, but are rarely handled in an undergraduate statistics course. These include spatial-sampling methods, orientation data, regionalised variables, time se­ ries, identification of cyclicity and pattern, discrimination, multivariate systems, lurking variables and constant-sum data. It is remarkable that most earth-science students confront these issues without formal training or focused consideration.

Recurso en línea: http://link.springer.com/openurl?genre=book&isbn=978-3-642-07815-6
Etiquetas de esta biblioteca: No hay etiquetas de esta biblioteca para este título. Ingresar para agregar etiquetas.
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Estado Fecha de vencimiento Código de barras
Libros Biblioteca Electrónica
Recursos en línea (RE) 		Acervo General Recurso digital ECO400565149104

Incluye bibliografía e índice: páginas 337-343

1. Spatial sampling.. 2. Central tendency and dispersion.. 3. Theoretical distributions: binomial, poisson and normal distributions.. 4. Statistical inference: estimation and hypothesis tests.. 5. Comparing frequency-distribution curves.. 6. Regression: linear, curvilinear and multilinear.. 7. Correlation and comparison of variables.. 8. Sequences, cycles and time series.. 9. Circular orientation data.. 10. Spherical-orientation data.. 11. Spherical orientation data: tensors.. 12. Appendix.. 13. References.. Index

Disponible para usuarios de ECOSUR con su clave de acceso

The Goals of Data Collection and Its Statistical Treatment in the Earth Sciences The earth sciences are characterised by loose and complex relationships between variables, and the necessity to understand the geographical dis­ tribution of observations as well as their frequency distribution. Our fre­ quency distributions and the looseness of relationships reflect the com­ plexity and intrinsic natural variation in nature, more than measurement error. Furthermore, earth scientists cannot design experiments according to statistical recommendation because the availability and complexity of data are beyond our control. Usually, the system we are studying cannot be isolated into discrete independent variables. These factors influence the first steps of research, how and where to collect specimens or observations. Some issues are particularly troublesome and common in earth science, but are rarely handled in an undergraduate statistics course. These include spatial-sampling methods, orientation data, regionalised variables, time se­ ries, identification of cyclicity and pattern, discrimination, multivariate systems, lurking variables and constant-sum data. It is remarkable that most earth-science students confront these issues without formal training or focused consideration. eng

Disponible en línea

Adobe Acrobat profesional 6.0 o superior

Subscripción a ELSEVIER 26 de diciembre del 2013

Con tecnología Koha