This course provides a broad overview of statistical methods and space-time data analysis frequently used in environmental science and studies. The topics covered in this course aim to provide you with the foundation and tools needed to empirically evaluate data
Expected learning outcomes
At the end of the course the student must be able to perform autonomously statistical analyses of environmental data, often having a space and/or time structure. The student must also be able to produce effective reports of the analysis.
Environmental Sampling. Models for Data (Statistical Models, Discrete Distributions, Continuous Distributions) Regression-type models and methods. Panel data analysis. Time Series Analysis. Spatial-Data Analysis (Point Patterns, Correlation between Variables in Space, Kriging)
Prerequisites for admission
Prerequisites for this course include a good knowledge of the mathematical tools presented in Calculus I, Linear Algebra and Basic Statistics courses (crash course)
Face-to-face lectures, tutorials
V. Barnett, Environmental Statistics. Methods and Applications. Wiley, 2004. Hill, Griffiths, Lim Principles of Econometrics. John Wiley & Sons, Inc Lecture Notes