Stefanini F.M., 2019, Introduzione ai metodi Bayesiani in statistica applicata. Class website.
Basic elements of Bayesian statistics.
Linear and logistic regression models for univariate responses. Foundations of experimental design. Competence acquired:
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 summaries; summarizing the key features of the investigated phenomenon.
2. Data analysis using the R software.
3. Fitting linear models. 4. Using statistical principia in designing simple experiments.
Courses to be used as requirements (required and/or recommended)
Courses required: none
Courses recommended: basic calculus.
Frequency of lectures, practice and lab, although non compulsory, is strongly recommended
Type of Assessment
Written or/and oral test on subjects of lectures, webinars , laboratory assignments and homework.
How to study for the final exam, the R software. Frequencies distributions, moments, quantiles. Graphical and numerical univariate and multivariate summaries.
Probability calculus and common random variables: Bernoulli, Binomiale, Normal, Poisson, Multinomial, Beta and Gamma families. Introduction to Bayesian subjective methods. Decision making, utility and risk. Linear models: estimation and testing with qualitative and/or quantitative explanatory variables.
Randomized controlled experiments: random sampling, randomization, control, replication, target and baselines variables.