Ateneo - UPO

The course aims at presenting the main statistical techniques for data analysis in view of biological applications. The course begins with an introduction to basic exploratory statistics, and some notions of probability. Then, the lectures will cover the main topics of applied statistics for biologic and medical applications, i.e., Student's t test for difference of means, analysis of variance, regression and analysis of contingency tables.

M. C. Whitlock, D. Schluter: Analisi statistica dei dati biologici, Zanichelli.
M. C. Whitlock, S. Shluter: The Analysis of Biological Data, Roberts & Co.

We expect that the student is able to read, to understand, and to comment properly the basic statistical reports to be found in common biological and medical literature. We expect that the student is able to apply the correct statistical analysis in different contexts, depending on the type of the collected data. The student must be able to read and use simple statistical analyses produced by means of specialised statistical software.

Basic Mathematics.

Lectures and laboratory sessions carried out also by means of the use of the University Moodle platform.

Learning monitoring: activities supported by the use of the Moodle platform of the University. These activities have a formative goal: they are discussed and corrected together with the students.
Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-students-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The exam consists of a written test containing exercises and one or more questions on the theoretical part. The exam is considered passed if the overall score is at least 18 points. To pass the exam, students must have passed the written test, demonstrating knowledge and understanding of at least the fundamental concepts and the most important related results. To achieve a high score, students must demonstrate independent judgment and critical thinking regarding the topics covered. Grades are expressed out of 30 (minimum score 18).

1. Descriptive statistics. The data. Populations and samples. Statistical variables. Graphical display of statistical variables: barplot, histograms, boxplots. Summary statistics: measures of central tendency and dispersion.
2. Probability. Definition of probability function. Conditional probability. The binomial model. The normal (Gaussian) distribution. The Poisson distribution.
3. Introduction to statistical inference. Point estimation and confidence interval for mean and variance.
4. Student's t-test 5. Linear regression. Simple linear regression. Computation of the regression line. Tests of significance of the coefficients.
6. Categorical data analysis. Chi-square goodness-of-fit tests. Contingency tables and chi-square independence test.
7. Discussion of real-data examples and case studies through the free software R.

- Knowledge and understanding: acquisition of knowledge on the main techniques of descriptive and inferential statistics, knowledge of the applications of these techniques to problems typical of the biological, biomedical and environmental fields, acquisition of appropriate scientific language.

- Applying knowledge and understanding: ability to read and analyze simple statistical analyses commonly reported in the biological and biomedical literature, ability to identify the correct statistical technique based on the experiment and the available data. The student must also be able to describe in a clear and concise way the techniques and their applications through an appropriate scientific language.