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Data management and research methodologies


Data management and research methodologies


Anno accademico 2018/2019

Codice dell'attività didattica
Prof. Mario GIACOBINI (Affidamento interno)
Prof. Dario SACCO (Affidamento interno)
Corso di studi
[1707M21-001] SCIENZE ANIMALI - curr. Animal Nutrition and Feed Safety
2° anno
B - Caratterizzante
SSD dell'attività didattica
INF/01 - informatica
Modalità di erogazione
Lingua di insegnamento
Modalità di frequenza
Tipologia d'esame
Basic knowledge of mathematics acquired during high school and during the course for getting the bachelor’s degree.
Students are required to have skills in the use of spreadsheets for i) parametrization of existing formulas, ii) creation of tables, and iii) creation of simple graphs (bar plots, scatter plots).
Propedeutico a
This is an apical course, preparatory for the profession.

Sommario insegnamento


Obiettivi formativi

The subjects are included in the learning area of knowledg and understanding capacity.

Objective of the course is to provide graduated students with expertise aimed at applying the scientific method and interpreting complex problems referred to animal nutrition issues. The student will be able to design and execute statistical procedures normally applied on technical and scientific journals.


Risultati dell'apprendimento attesi

Knowledge and understanding

Students will be able:

  • to learn basic notions of inferential statistics, mastering the concepts of hypothesis, distribution, and inference;
  • to interpret simple univariate statistics, normally appearing on technical and scientific journals;
  • to understand experimental results based on statistical evidence.

Applying knowledge and understanding

They will be also able to perform the same statistics by themselves using worksheets or statistical software and plan simple experimental designs. Students will be able:

  • to manage simple flat databases deriving main descriptive statistics;
  • to represent results by means of tables and figures;
  • to plan simple experimental designs;
  • to draw the statistical framework useful to study the phenomenon under investigation, by choosing and applying the statistical test more adequate for the selected statistical analysis.

Making judgements

Students must gain a good confidence in selecting the experimental design more suitable to study the phenomenon under investigation. Once conducted the statistical analysis of the data collected, students must show a good autonomy in discussing experimental results based on statistical outputs.

Communication skills

Students will acquire a proper vocabulary for describing statistical results.

Learning skills

The acquired knowledge offers the basis to independently analyse data and for a future learning of statistical models.


Modalità di insegnamento

The course consists of 60 hours of lectures carried out on PC working stations. PowerPoint presentations and already solved exercises, used during lessons, are available to students on the course platform.
Attendance to the course is not mandatory, but students are warmly request to attend. The final exam will not be differentiated between students that have attended and those that have not.


Modalità di verifica dell'apprendimento

Final exam will consists of exercises to be solved with the help of a PC and of a oral part to assess theoretical knowledge of the student. Practical part must be evaluated "sufficient" to have access to the oral part. It will be expressed on a scale of 30.

During the semester two ongoing practical tests are scheduled on the first (30 hours) and second (30 hours) parts of the course. If passed with a sufficient mark, the ongoing practical tests exonerate from the parts covered at the final examinations.



Course program:

  • Types of variables
  • Descriptive statistics: indices of position, variability and shape.
  • Probability distributions and representations.
  • Calculation of probability using statistical software
  • Application on inferential statistics
  • Central limit theorem and statistical hypothesis testing
  • Standard error and confidence intervals
  • Samples comparison
  • First and second type errors
  • One way and multiple ways ANOVA
  • Factorial design
  • Data transformation for normality tests
  • Simple linear regression
  • Correlation analysis
  • Multiple linear regression
  • Introduction to LMM

Testi consigliati e bibliografia


Statistics: The Art and Science of Learning from Data, 4th Edition
Alan Agresti, Christine A. Franklin, Bernhard Klingenberg
Pearson, ISBN: 9780133860917

Ultimo aggiornamento: 31/10/2018 12:10
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