The course is organized into lectures and computer's practical session using R software, that represents the most versatile statistical data analysis program used in science. The main objective of the course is to provide the theoretical and practical skills needed to manage and analyze data in the biological and environmental field, with a rigorous scientific approach. The analysis and interpretation of experimental data will be addressed both through theoretical lessons and practical computer sessions. In fact, the course aims to introduce students to the use of open source software R both for the manipulation, analysis and graphical representation of data.
Teaching Methods
A theoretical and practical approach will be adopted. R software will be used.
Assessment Methods
The exam will be written and will consist of exercises and multiple choice questions. The exam will last two hours. The student must demonstrate not only knowledge of statistical data analysis techniques but also the ability to interpret the results obtained.
Texts
MC Whitlock, D Schluter STATISTICAL ANALYSIS OF BIOLOGICAL DATA. Zanichelli publisher
Contents
The teaching activity will be organized in such a way as to encourage active learning and a more participatory role of the student also thanks to the practical parts carried out using PCs which can undoubtedly represent a moment of discussion and communication, also promoting critical thinking and improving the ability to work in a group.
Specifically, the following topics will be covered:
1.- STUDY DESIGN, SCIENTIFIC RESEARCH PHASES, STUDY CLASSIFICATION AND DATA COLLECTION
2- INTRODUCTION TO THE R ENVIRONMENT FOR DATA ANALYSIS (CREATION OF OBJECTS AND ARRAYS, MANIPULATION OF DATA, IMPORT AND EXPORT OF DATA FILES)
3- DESCRIPTIVE STATISTICS I (FREQUENCY DISTRIBUTIONS, MEASUREMENTS OF POSITION AND DISPERSION)
4- GRAPHICAL REPRESENTATION OF DATA WITH R
6- SENSITIVITY AND SPECIFICITY OF A TEST
7- PARAMETRIC AND NON-PARAMETRIC STATISTICAL TESTS (HOW AND WHEN TO USE THEM. THEORETICAL DEFINITIONS AND APPLICATIONS IN R)
8- STATISTICAL INFERENCE (THE DIFFERENT HYPOTHESIS TESTS, THEORETICAL DEFINITIONS AND APPLICATIONS IN R)
9- CORRELATION, LINEAR AND LOGISTIC REGRESSION (THEORETICAL DEFINITIONS AND APPLICATIONS IN R)
Lessons (both theoretical and practical) will be available on the platform KIRO
