Statistics

Statistics provides a powerful approach to make sense of data and to take into account the uncertainties which come from the randomness of complex systems. The course presents some statistical tools to analyse weather/climate data with a focus on regression approaches which are particularly suited to explore how several variables can affect climate systems.
The course is part of a three-course statistics path which is offered to doctoral students of the Department: the path is composed of a first introductory course (PHD154 Data Analysis with Statistical Programming) which precedes two more advanced non-sequential course (PHD076 Statistical Methods for Climate Change Analysis and PHD124 Applied Time Series). Students who are interested in gaining a more solid background in statistical sciences are highly encouraged to follow all courses and to discuss with the instructor of the course for their PhD program the best course of action.

Students will be able to correctly carry out a statistical analysis of climate-related variables using a statistical software, identifying the most suitable statistical approach for the problem under study and identifying potential benefits and pitfall of various analytical approaches.

No formal pre-requisite. The course will make use of some mathematics and statistics concepts such as functions, integrals, derivatives, matrices, distributions, estimation and hypothesis testing so students should have the background of an elementary statistics course (at STAT-100 level). It is also expected that students have some notions of how to use R. Students who feel they do not have the necessary familiarity with these topics are highly encouraged to follow the course PHD154 Data Analysis with Statistical Programming which is offered in the Environmental Sciences PhD program and to discuss with the course instructor the best course of action for their learning.

The course deals with analysing the statistics of weather and climate by taking uncertainties and random fluctuations into account and providing objective assessment of various statistical hypotheses. The course will mostly focus or regression-based approaches to analyse climate data. The course content will discuss standard linear regression models, generalised Linear models (with emphasis on Poisson and Logistic regression) and their extensions (Generalised additive models, hierarchical models, ...). Students will also be encouraged to identify statistical approaches used in scientific literature which can be further discussed and evaluated during the course. The practical aspects of all methods discussed in class will be discussed with case studies and applications using the R programming language. The course will focus mostly on the use of regression models: students with a deeper interest in time series models are encouraged to also follow the PHD124 (Applied Time Series) course: students who want to do this are encouraged to discuss the matter with the course instructor.

Lecture notes and additional external material identified by the instructor. Furthermore, these text might prove a useful reference

Daniel S. Wilks, Statistical Methods in the Atmospheric Sciences, 2005, Academic Press

Simon Wood, Generalized Additive Models: An Introduction with R, Second Edition, 2017, CRC Press

1) Class participation and homeworks 20%
Students are expected to play an active role during the course and the labs and to hand in a periodic assessed homework.

2) Project 50%
Each student will be assigned a data set about a climate-change problem to analyse (students are encouraged to suggest a dataset they are wish to analyse). The outcome of the analysis needs to be detailed in a short report.

3) Final exam 30%
The final exam consists of an oral discussion of the project.

Theoretical lectures complemented by lab classes. The statistical software used in the course is R (www.r-project.org).

English

oral

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development