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.

Gli studenti saranno in grado di effettuare correttamente un'analisi statistica delle variabili ambientali e climatiche utilizzando software statistici, individuando l'approccio statistico più adatto al problema oggetto di studio e identificando potenziali benefici e insidie dei vari approcci analitici.

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.

Basic R programming. RStudio and R Markdown for reproducible research. 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.

Book open source on R and data analysis
Scientific manuscripts
Slides provided 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.

written

- 27-30: Correct answers; appropriate use of technical language; excellent presentation of statistical analysis results in terms of analysis accuracy, result visualization/presentation, and code readability.
- 22-26: Generally correct answers, though sometimes expressed with not entirely appropriate and/or proficient use of technical language; satisfactory presentation of statistical analysis results, with largely correct analyses, good result visualization/presentation, and generally readable and executable code.
- 18-21: Only partially correct answers, with inconsistent and sometimes inappropriate use of technical language; fundamentally correct statistical analyses, though presented in a not entirely satisfactory manner, with code that is not always fully readable and/or executable.

Teaching will be organized in:
a) lessons on the main theoretical concepts and the description of the various methods;
b) exercises in which the theoretical concepts are put into practice, writing code, analyzing data, interpreting and communicating the results.