First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Agricultural Sciences and Veterinary Medicine
Course unit
AGO2042490, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
AG0057, Degree course structure A.Y. 2017/18, A.Y. 2018/19
bring this page
with you
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 FOOD SCIENCE AND TECHNOLOGY (Ord. 2017)

Teacher in charge CRISTINA SARTORI 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 1.0 8 17.0 No turn
Lecture 3.0 24 51.0 No turn

Start of activities 01/10/2018
End of activities 18/01/2019
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Board From To Members of the board
7 Commissione a.a. 2018/19 01/12/2018 30/11/2019 SARTORI CRISTINA (Presidente)
PENASA MAURO (Supplente)
6 Commissione a.a. 2017/18: dal 21/05/2018 al 30/10/2018 21/05/2018 30/10/2018 MANTOVANI ROBERTO (Presidente)
PENASA MAURO (Supplente)

Prerequisites: None
Target skills and knowledge: The course will introduce the student of the First cycle degree in Food science and technology to the main statistic technics applied in field studies and experimental designs of various disciplines, focusing on both theoretical background and on the practical application.
The course aim to provide effective knowledge for:
- data management;
- clear and effective data representation;
- critical reading of datasets;
- decision making process;
- planning of experimental design.
Examination methods: The exam will consist in short or multiple choice questions and exercises on the lessons' topics
Assessment criteria: Student preparation will be evaluated on the basis of his knowledge of theoretical principles and application of statistical methodologies considered in the course
Course unit contents: Introduction to statistics: descriptive statistics and inference. Population, sample and statistic unit.
Descriptive statistics: variables, central tendency indicators and measures of dispersions; frequencies, graphical representation of data.
Basics of probability and probability distributions: the Normal (Gaussian) Distribution.
From descriptive statistics to inference: experimental design, sampling, choice of a proper hypothesis test.
Confidence intervals and hypothesis testing. Variances and mean comparisons for one sample, two samples, paired data.
One-way analysis of variance (ANOVA) for comparing 2 or more populations.
Categorical data hypothesis testing using Chi-square statistics; contingency tables.
Correlation and linear regression.
Planned learning activities and teaching methods: Frontal lessons and exercitations using slides and material previously provided to students.
Additional notes about suggested reading: Slides and material provided by teacher, including examples of exercises with solutions
Textbooks (and optional supplementary readings)
  • Fowler, J. Cohen, L., Statistica per Ornitologi e Naturalisti. Roma: Franco Muzzio Editore, 2002. Cerca nel catalogo
  • Levine, D.M., Krehbiel T. C., Berenson M.L., Statistica. Milano: Pearson, 2010. Cerca nel catalogo
  • Triola, M.M., Triola, M.F, Fondamenti di Statistica per le discipline biomediche. Milano: Pearson, 2013.

Innovative teaching methods: Teaching and learning strategies
  • Case study
  • Questioning
  • Problem solving
  • Auto correcting quizzes or tests for periodic feedback or exams
  • Active quizzes for Concept Verification Tests and class discussions
  • Loading of files and pages (web pages, Moodle, ...)

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

Sustainable Development Goals (SDGs)
Quality Education