Stefanini F.M., 2018, Introduzione ai metodi Bayesiani in statistica
applicata: materiale ausiliario (sito web)
Lindley D.V., 2000, The Philosophy of Statistics, The Statistician, 49(3):
Basic elements of Bayesian statistics.
Linear and logistic regression models for univariate responses.
Foundations of experimental design.
Competence acquired (at the end of the course);
Recognizing the nature of variables investigated during the study of a
phenomenon. Evaluation of critical features characterizing a designed
experiment. Selection of suitable statistical techniques to perform the
analysis of experimental results.
Skills acquired (at the end of the course):
1. Assessment of raw data quality by means of suitable indices;
summarizing the key features of the investigated phenomenon.
2. Data analysis using the R software.
3. Fitting of linear and logistic regression models.
4. Design of experiments.
Courses to be used as requirements (required and/or recommended)
Courses required: none
Courses recommended: basic calculus.
Contact hours for:
Lectures:48 (webinars and classroom)
Type of Assessment
Oral exam on subjects of lectures, laboratory assignments and
How to study for the final exam, the R software. Frequencies
distributions, moments, quantiles. Graphical univariate and multivariate
Probability calculus and common random variables: Bernoulli, Binomiale,
Normal, Poisson, Multinomial, Beta and Gamma families. Introduction to
Bayesian subjective methods. Linear and logistic regression models:
estimation and testing with qualitative and/or quantitative explanatory
Randomized controlled experiments: random sampling, randomization,
control, replication, target and baselines variables, random blocks, full
factorial designs, Balanced Incomplete Blocks designs.