An introduction to the most commonly used statistical methods in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, with statistical and scientific concepts explained as needed. Requiring no previous background in statistics, it is an accessible reference for students in the climate sciences.
An introduction to the most commonly used statistical methods in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, with statistical and scientific concepts explained as needed. Requiring no previous background in statistics, it is an accessible reference for students in the climate sciences.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Timothy M. DelSole is Professor in the Department of Atmospheric, Oceanic, and Earth Sciences, and Senior Scientist at the Center for Oceanic Atmospheric, and Land Studies, at George Mason University, Virginia. He has published over one hundred peer-reviewed papers in climate science and served as co-Editor-in-Chief of the Journal of Climate.
Inhaltsangabe
1. Basic Concepts in Probability and Statistics 2. Hypothesis Tests 3. Confidence Intervals 4. Statistical Tests Based on Ranks 5. Introduction to Stochastic Processes 6. The Power Spectrum 7. Introduction to Multivariate Methods 8. Linear Regression: Least Squares Estimation 9. Linear Regression: Inference 10. Model Selection 11. Screening: A Pitfall in Statistics 12. Principal Component Analysis 13. Field Significance 14. Multivariate Linear Regression 15. Canonical Correlation Analysis 16. Covariance Discriminant Analysis 17. Analysis of Variance and Predictability 18. Predictable Component Analysis 19. Extreme Value Theory 20. Data Assimilation 21. Ensemble Square Root Filters 22. Appendix References Index.
1. Basic Concepts in Probability and Statistics 2. Hypothesis Tests 3. Confidence Intervals 4. Statistical Tests Based on Ranks 5. Introduction to Stochastic Processes 6. The Power Spectrum 7. Introduction to Multivariate Methods 8. Linear Regression: Least Squares Estimation 9. Linear Regression: Inference 10. Model Selection 11. Screening: A Pitfall in Statistics 12. Principal Component Analysis 13. Field Significance 14. Multivariate Linear Regression 15. Canonical Correlation Analysis 16. Covariance Discriminant Analysis 17. Analysis of Variance and Predictability 18. Predictable Component Analysis 19. Extreme Value Theory 20. Data Assimilation 21. Ensemble Square Root Filters 22. Appendix References Index.
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