Elaborazione dei dati sperimentali

Ecological Data Management and Analysis

Anno accademico 2017/2018

Sommario insegnamento

• Obiettivi formativi
• Risultati dell'apprendimento attesi
• Modalità di insegnamento
• Modalità di verifica dell'apprendimento
• Programma
• Testi consigliati e bibliografia
• Strumenti didattici

Obiettivi formativi

This course intends to gently introduce data management and statistical analysis techniques and concepts to students in the Natural Sciences. While we will mostly use examples in the fields of Animal Ecology, the course can be followed by students from other scientific domains.

The first part of the course describes good practices to handle scientific data. The aim is to help students to organize data properly, building tidy datasets that could be subsequently used to perform statistical analysis, with any software. In the second part of the course, we will explain basic concepts behind statistical tools and we will introduce hypothesis testing using interesting ecological examples. The course will not be heavy on mathematics and statistical theory, trying instead to practically apply all the acquired concepts and methods to real data sets. To achieve this goal, we will use the open-source statistical language “R” and the friendly “RStudio” interface.  Specifically developed tutorials and interactive exercises will assist the student during the whole learning process. 

Risultati dell'apprendimento attesi

At the end of the course it is expected that students will have acquired:

1. the essential tools to correctly manage and process field and experimental data before analysis;
2. a sufficient “statistical literacy” to autonomously judge which methods are most appropriate to use in a range of data analysis problems typically encountered in the natural sciences;
3. a good understanding of statistical concepts and methods;
4. the ability to apply the learned theoretical concepts and methods to their own data using the powerful statistical language “R”;
5. the confidence to autonomously explore further more complex approaches;

Modalità di insegnamento

The lessons will be held in the classroom and each of them will be divided into a frontal lesson, designed to illustrate the basic concepts, and in an exercise session, in order to immediately apply the statistical concepts to real case studies. Appropriate IT equipment is therefore necessary to follow the course.

Modalità di verifica dell'apprendimento

70% of the final note will be based on a written exam with multiple choice answers and short statistical problems to solve using “R” (similar to those you will encounter as exercises during the course, but generally easier!). The remaining 30% of the final note will be based on an oral discussion concerning the main concepts encountered during the course. During the oral examination, students will be asked to discuss the answers to the written exam, and to comment on the R exercise.

Programma

Data management

• What is data and how to handle it;
• What is R and how to use it;
• Tidy data;
• Data exploration

Data analysis

• Review of basic statistical concepts: sampling distributions and their properties;
• Confidence intervals, p-values and hypothesis testing;
• z and t distributions: the t-tests;
• Errors, power and nonparametric statistics;
• Simple linear regression and ANOVA

There will not be any official textbook for this course, but open access materials, tutorials and relevant review papers will be made available on the Moodle platform.

Orario lezioni

• Materiale didattico
• Bacheca appelli
• Vai a Moodle
• Visita i forum