Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment.
Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases.
1. Environmental Data Analysis with MATLAB® or Python - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Preface - p. Page v 7. Acknowledgment - p. Page xi 8. Advice on scripting for beginners - p. Page xii 9. List of Illustrations - p. Page xiii 10. List of Tables - p. Page xiv 11. Chapter 1 : Data analysis with MATLAB® or Python - p. Page xiv 12. Chapter 2 : Systematic explorations of a new dataset - p. Page 46 13. Chapter 3 : Modeling observational noise with random variables - p. Page 73 14. Chapter 4 : Linear models as the foundation of data analysis - p. Page 110 15. Chapter 5 : Least squares with prior information - p. Page 140 16. Chapter 6 : Detecting periodicities with Fourier analysis - p. Page 166 17. Chapter 7 : Modeling time-dependent behavior with filters - p. Page 200 18. Chapter 8 : Undirected data analysis using factors, empirical orthogonal functions, and clusters - p. Page 237 19. Chapter 9 : Detecting and understanding correlations among data - p. Page 271 20. Chapter 10 : Interpolation, Gaussian process regression, and kriging - p. Page 315 21. Chapter 11 : Approximate methods, including linearization and artificial neural networks - p. Page 347 22. Chapter 12 : Assessing the significance of results - p. Page 380 23. Chapter 13 : Notes - p. Page 411 24. Index - p. Page 439
Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment.
Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases.
1. Environmental Data Analysis with MATLAB® or Python - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Preface - p. Page v 7. Acknowledgment - p. Page xi 8. Advice on scripting for beginners - p. Page xii 9. List of Illustrations - p. Page xiii 10. List of Tables - p. Page xiv 11. Chapter 1 : Data analysis with MATLAB® or Python - p. Page xiv 12. Chapter 2 : Systematic explorations of a new dataset - p. Page 46 13. Chapter 3 : Modeling observational noise with random variables - p. Page 73 14. Chapter 4 : Linear models as the foundation of data analysis - p. Page 110 15. Chapter 5 : Least squares with prior information - p. Page 140 16. Chapter 6 : Detecting periodicities with Fourier analysis - p. Page 166 17. Chapter 7 : Modeling time-dependent behavior with filters - p. Page 200 18. Chapter 8 : Undirected data analysis using factors, empirical orthogonal functions, and clusters - p. Page 237 19. Chapter 9 : Detecting and understanding correlations among data - p. Page 271 20. Chapter 10 : Interpolation, Gaussian process regression, and kriging - p. Page 315 21. Chapter 11 : Approximate methods, including linearization and artificial neural networks - p. Page 347 22. Chapter 12 : Assessing the significance of results - p. Page 380 23. Chapter 13 : Notes - p. Page 411 24. Index - p. Page 439
