INTRODUCTORY 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. To provide PHD students with the most suitable statistical training for their research needs and to adapt to the different background and previous experiences this course is part of a 4-course series which is available to doctoral students of the Department. The courses are

* Introductory Statistics
* Regression models and distribution fitting
* Statistical models: generalised linear models and extensions
* Spatio-temporal statistical models

Each course is worth 3 credits. Students from the Science and Management of Climate Change must take (at least) two courses. Students from the Environmental Sciences program can take as many courses as needed. Students who are interested in gaining a more solid background in statistical sciences are highly encouraged to follow all courses.

It is recommended that all students discuss with the instructors of the courses, their PhD supervisor and/or the PhD director the most suitable combination of courses to take for their PhD plan.

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

No formal pre-requisites. The course will make use of some mathematics and statistical concepts such as functions, integrals, derivatives, matrices, distributions, estimation and hypothesis testing. It is also expected that students have some notions of how to use R or some other data analysis software (Stata, Python, Matlab).

RStudio. R Markdown for reproducible research. Basic R programming. Logical expressions. Vectors, matrices, data frames, lists. Reading, writing, editing data. Read data downloaded from the internet. Conditional execution. Loops. Recursion. Fast R code and vectorization. Descriptive statistics. Plotting. Cases studies.

Open source books on R
Scientific papers
Lecture notes given by the lecturer

The examination will take place in the computer lab. Students will be asked to answer a few written questions and to carry out a data-analysis task, providing a small report on their analysis. Students must achieve a sufficient ranking in both parts to pass the overall exam. The grade will be based on the number of correct answers provided in the written questions and on the level of the presentation for the data analysis task. In particular, the following items will be evaluated: clarity of the presentation and, appropriateness of the statistical approaches and data visualization methods, readability of the code.

The course comprises of
a) theoretical lessons describing the various concepts and methods
b) practicals with data analyses, code programming and result discussion and communication.

English

None.

written