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- Author: Hans Gilgen
- Publisher: Springer
- Year: 2010
- Pages: 718
- ISBN-10: 3642062768
- ISBN-13: 9783642062766
- Format: 15.6 x 23.4 x 3.7 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
The author introduces the statistical analysis of geophysical time series. The book includes also a chapter with an introduction to geostatistics, many examples and exercises which help the reader to work with typical problems. More complex derivations are provided in appendix-like supplements to each chapter. Readers are assumed to have a basic grounding in statistics and analysis. The reader is invited to learn actively from genuine geophysical data. He has to consider the applicability of statistical methods, to propose, estimate, evaluate and compare statistical models, and to draw conclusions.
The author focuses on the conceptual understanding. The example time series and the exercises lead the reader to explore the meaning of concepts such as the estimation of the linear time series (AMRA) models or spectra.
This book is also a guide to using R for the statistical analysis of time series. R is a powerful environment for the statistical and graphical analysis of data.R is available under GNU conditions.
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- Author: Hans Gilgen
- Publisher: Springer
- Year: 2010
- Pages: 718
- ISBN-10: 3642062768
- ISBN-13: 9783642062766
- Format: 15.6 x 23.4 x 3.7 cm, minkšti viršeliai
- Language: English English
The author introduces the statistical analysis of geophysical time series. The book includes also a chapter with an introduction to geostatistics, many examples and exercises which help the reader to work with typical problems. More complex derivations are provided in appendix-like supplements to each chapter. Readers are assumed to have a basic grounding in statistics and analysis. The reader is invited to learn actively from genuine geophysical data. He has to consider the applicability of statistical methods, to propose, estimate, evaluate and compare statistical models, and to draw conclusions.
The author focuses on the conceptual understanding. The example time series and the exercises lead the reader to explore the meaning of concepts such as the estimation of the linear time series (AMRA) models or spectra.
This book is also a guide to using R for the statistical analysis of time series. R is a powerful environment for the statistical and graphical analysis of data.R is available under GNU conditions.
Reviews