Learning objectives
Course learning objectives are to introduce students to the methods of descriptive and inferential statistics, which have many potential applications in animal husbandry as well as veterinary sciences.
Methods are introduced in an application-oriented manner, taking advantage of computer-based tools for modeling, analysis and visualization of data.
More specifically, the course aims at equipping students with tools expanding their portfolio of professional skills as expert in animal husbandry, including proper use of statistics, graphical presentation of data and populations, inference and forecasting of meaningful statistical data.
At the end of the course, the student will be able to make autonomously decisions based on quantitative information, thereby improving the quality level of his/her professional environment. (Dublin descriptors I, II, III).
Moreover, at the end of the course, the student will be able to effectively communicate and interact leveraging upon what has been learned, with prospective customers as well as with colleagues in the profession.
He/she will be able to highlight proper decisions in the frame of animal husbandry using charts, easy-to-understand statistical representations, probability assessment, and confidence intervals.
Finally, the course aims at stimulating in students their critical thinking potential, including the attitude to question consolidated but possibly outdated practices in husbandry, or to challenge what is being learned in class (Dublin descriptors IV and V).
Prerequisites
No formal prerequisites.
Course unit content
The course comprises three main parts.
Part 1 provides a general view of statistical methods to describe populations, samples and individual subjects. These methods include frequency tables, charts, and meaningful statistical descriptors such as mean, median, and standard deviation. Methods to compare and relate variables are also proposed.
Part 2 provides practical tools and examples of probability theory, enabling its practical application in several animal husbandry and veterinary contexts.
Part 3 introduces estimation techniques of statistical measures over entire populations starting from suitably extracted population samples. Fundamental concepts that will be presented include confidence interval estimation and hypothesis testing, as well as other estimation techniques.
Presentation of all topics is supported by exercises and examples during each lecture as well as in dedicated lectures, based on spreadsheet tools offering statistical functions, such as Microsoft Excel.
Two lectures will be dedicated, possibly with contribution from industrial guest speakers, to illustration of information systems and other computer engineering tools currently adopted in animal husbandry data management.
Full programme
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Bibliography
A good textbook on statistics is a valuable resource not only for passing the course exam but also for the profession after graduation.
Two broadly used italian textbooks on Statistics are the following:
G. Cicchitelli, P. D'Urso, M. Minozzo, "Statistica: principi e metodi", Pearson, 2017. ISBN: 8891902780.
F. Mencatti, "Statistica di base", III edizione, McGraw-Hill, 2022. ISBN: 8838656606.
However, many other textbooks, possibly in English if preferred, can serve the purpose.
Students regularly attending classes are not required to purchase a textbook for the purpose of the exam, whereas a textbook provides a comprehensive treatment of the subject an may be needed to students who do not regularly attend classes.
Additional material, including copy of lecture slides, will be available to all students on the Elly web site of the class.
Teaching methods
Lectures in classroom at the Department of Veterinary Sciences.
Assessment methods and criteria
Exam is in two parts: written test followed by interactive practical test using Excel (same day or next working day, if necessary).
Written test with multiple quizzes and simple exercises, closed books: max 30 points, 1 hour duration.
Students which obtain at least 15/30 in the written test are admitted to the interactive practical test.
Quiz evaluation and marking occurs shortly after the written test conclusion, and is immediately followed by the practical segment of the exam.
Interactive practical test with exercises to be solved with Excel (20 minute duration): max 30 points.
Exercises are similar to those presented in classes.
Final mark is the average of the two individual evaluations, provided that each assessment reaches a quasi-pass level (minimum threshold is 15/30) and the average is above the threshold 18/30. The honor mark (30/30 e lode) is assigned if the average reaches 30 and the candidate performs very well in terms of clarity of exposition.
Disabled students can receive extra time to cope with the test.
Other information
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2030 agenda goals for sustainable development
4 Quality education
12 Sustainable consumption and production
