Biostatistics and Bioinformatics
A.Y. 2024/2025
Learning objectives
The objective of the course is to teach the student the basic knowledge of biostatistics and bioinformatics. Such knowledge will be fundamental for a proper experimental design and data analysis, as well as for statistical interpretation and evaluation of experimental results.
Expected learning outcomes
At the end of the course, the students will acquire a good knowledge about the main statistical tests and models that are used in biology and animal science and their will be able to select the proper ones for the experiments and analysis in the field of interest.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Lectures will be held on the Microsoft Teams platform, even for those provided in presence, and can be followed both in synchronous on the basis of the first scheduled lectures and in asynchronous because they will be recorded and left available to students on the same platform. Exams, if it is not possible to attend the classroom, will be performed orally through the Microsoft Teams platform.
Course syllabus
Frontal teaching (32 hours):
- Data, variables, distributions, their presentation and synthesis (3 hours);
- Descriptive statistics - Position and variability indicators (4 hours);
- Hypothesis testing in statistics (3 hours);
- Inferential statistics - Frequency analysis, χ2 distribution and statistical test (3 hours);
- Analysis of biological data normal distribution, standardized normal distribution, use of normal distribution tables (3 hours);
- The difference between means of a sample and inference of results in the population, t-test (2 hours);
- Correlation and correlation absence test (1 hour);
- Solving systems of equations with matrix algebra - linear models (3 hours)
- Linear regression and hypothesis testing with ANOVA (4 hours);
- ANOVA through the use of linear models (6 hours);
Practical labs (32 hours):
Application of the topics of the course on real datasets through the use of Excel and RStudio.
- Data, variables, distributions, their presentation and synthesis (3 hours);
- Descriptive statistics - Position and variability indicators (4 hours);
- Hypothesis testing in statistics (3 hours);
- Inferential statistics - Frequency analysis, χ2 distribution and statistical test (3 hours);
- Analysis of biological data normal distribution, standardized normal distribution, use of normal distribution tables (3 hours);
- The difference between means of a sample and inference of results in the population, t-test (2 hours);
- Correlation and correlation absence test (1 hour);
- Solving systems of equations with matrix algebra - linear models (3 hours)
- Linear regression and hypothesis testing with ANOVA (4 hours);
- ANOVA through the use of linear models (6 hours);
Practical labs (32 hours):
Application of the topics of the course on real datasets through the use of Excel and RStudio.
Prerequisites for admission
No prerequisite
Teaching methods
The course is based on class frontal lectures and computer practice sessions. For the computer sessions, Microsoft Excel and R plus public domain software will be used. The software allows the management and statistical analysis of data useful for understanding the course topics.
Teaching Resources
-) Class-notes provided by the teacher
-) The R software for statistical computing" (https://www.r-project.org/) e Rstudio (https://www.rstudio.com/).
-) The R software for statistical computing" (https://www.r-project.org/) e Rstudio (https://www.rstudio.com/).
Assessment methods and Criteria
The exam will consist into 1 oral test.
Brief description of the test procedures:
The student will be asked to present a statistical analysis performed on a data set provided by the teacher. Specific topics of the course program will be also asked to the student during the oral exam
Brief description of the test procedures:
The student will be asked to present a statistical analysis performed on a data set provided by the teacher. Specific topics of the course program will be also asked to the student during the oral exam
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 6
Practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Bagnato Alessandro
Shifts:
Turno
Professor:
Bagnato AlessandroProfessor(s)