First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Agricultural Sciences and Veterinary Medicine
Course unit
AGO2042490, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course First cycle degree in
IF0325, Degree course structure A.Y. 2017/18, A.Y. 2019/20
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Number of ECTS credits allocated 4.0
Type of assessment Evaluation
Course unit English denomination APPLIED STATISTICS
Website of the academic structure
Department of reference Department of Agronomy, Food, Natural Resources, Animals and the Environment
E-Learning website
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit is available ONLY for students enrolled in ANIMAL SCIENCE AND TECHNOLOGY

Teacher in charge ALESSIO CECCHINATO AGR/17

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Other -- -- 4.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Practice 2.0 16 34.0 No turn
Lecture 2.0 16 34.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Board From To Members of the board
9 Commissione a.a. 2019/20 01/12/2019 30/11/2020 CECCHINATO ALESSIO (Presidente)
MANTOVANI ROBERTO (Membro Effettivo)
8 Commissione a.a. 2018/19 01/12/2018 30/11/2019 CECCHINATO ALESSIO (Presidente)
MANTOVANI ROBERTO (Membro Effettivo)

Prerequisites: None.
Target skills and knowledge: To introduce the student of the Degree in Animal Science and Technology to the main statistical techniques in application contexts where it arises spontaneously making their use. The course will focus both on the theoretical foundations and in their proper application to real problems.
The aim of the course is to provide the tools necessary to:
- Know how to manage a set of data,
- Represent a set of data in an effective way,
- Being able to read a data set in a critical way,
- Make choices,
- Be able to plan and carry out simple experiments.
Examination methods: Written test on theory and exercises.
Assessment criteria: The preparation of the student will be evaluated based on the degree of knowledge of the theoretical principles and applications of statistical methods presented within the teaching.
Course unit contents: Introduction to statistics: descriptive statistics and inferential statistics. The concepts of population, sample, statistical unit.
Descriptive statistics: types of variables, indicators of central tendency and variability or dispersion, the concept of frequency, graphic representations of data, correlation and contingency tables.
The main elements of probability and probability distributions. The Gaussian or normal probability distribution.
From descriptive statistics to inferential statistics: the concept of replication and simple random sampling.
The confidence intervals and hypothesis testing. One sample tests, two sample tests and tests for paired data.
The one-way analysis of variance for comparing C> 2 populations.
The hypothesis testing for categorical data using the chi-square test.
The simple linear regression.
Planned learning activities and teaching methods: Lectures, exercises and tutorials.
Additional notes about suggested reading: Slide and material provided by the teacher and textbook.
The course material is available at the link:
Textbooks (and optional supplementary readings)
  • Levine, D.M., Krehbiel T.C., Berenson M.L., Statistica. Milano: Pearson, 2010. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Working in group

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
No Poverty Zero Hunger Good Health and Well-Being Quality Education Gender Equality Clean Water and Sanitation Affordable and Clean Energy Decent Work and Economic Growth Industry, Innovation and Infrastructure Reduced Inequalities Sustainable Cities and Communities Responsible Consumption and Production Climate Action Life Below Water Life on Land Peace, Justice and Strong Institutions Partnerships for the Goals