D3a - Dipartimento di Scienze Agrarie, Alimentari e Ambientali - Guida degli insegnamenti (Syllabus)
Students will be guided through the course by structured lectures (2 ECTS) and relevant practical classes (1 ECTS) to acquire the principles of statistics and their most common applications. In parallel, an e-learning version of the course using the Moodle platform will be available, including teaching materials organized in learning units, exercises, self-evaluation tests and results.
Knowledge and skills. The aim of the course is to provide a basic information on statistical methods and biometry, for understanding the main features of data analysis and their proper application.
Applying knowledge and understanding. The aim of the course is to provide a basic information on statistics, for the understanding of the main applications and concept in order to enable a basic tool for experimental design, data analysis and data presentation. Moreover, the course will provide the tools to understand the most common statistical applications utilized in scientific and technical literature.
Cross-expertise: (i) acquisition of skills related to the sampling, experimental design and data analysis of various type of data; (ii) capability of understanding the meaning of the results of statistical analysis reported in the scientific and technical literature.
Introduction and descriptive statistics: The scientific method, Measurements of natural phenomena, Empirical distributions, Descriptive statistics, Variability and graphical representation of data (1 ECTS).
Inferential statistics: Populations and samples, Probability and hypothesis testing, Distribution of probabilities, Confidence intervals and mean comparisons, t test and Analysis of Variance (ANOVA), Correlation and regression analysis, Non-Parametric methods (2 ECTS).
Final assessment methods
"Principles of Economics and Statistics" is an integrated course consisting of two modules: Principles of Economics (6 ECTS) and Principles of Statistics (3 ECTS). Each module is independently evaluated, but with a unique final grade, resulting from the weighted average of the grades obtained in the two modules (weights results from the respective number of ECTS for each module).
Learning evaluation methods
Final assessment will consist of a written test based on exercises on of the subjects listed in the teaching program. During the course, will be available self-evaluation tests (e-learning mode), in order to provide the students useful information about their skill level.
Learning evaluation criteria
In the written test the student will have to complete successfully at least 3/5 of the exercises that will be designed to evaluate: a) the ability to utilize the most common descriptive statistics for data analysis (including graphical representation) applied to various types of data; b) understanding of the principles of probability and hypothesis testing; c) ability to use and understand the results of inferential statistical applications and d) the ability of designing simple statistical experiments.
Learning measurement criteria
The final mark is attributed in thirtieths. Successful completion of the examination will lead to grades ranging from 18 to 30 “cum laude”.
Final mark allocation criteria
The written test consists of five exercises concerning the subjects listed in the teaching program, each one will be evaluated within the range 0 - 6. The maximum grade 30 “cum laude” is attributed to students demonstrating complete mastery of the subject.
Michael C. Whitlock, Dolph Schluter, 2008. The Analysis of Biological Data, Roberts and Company Publishers
Thursday from 3 pm to 5 pm.