Stefanini F.M., 2019, Introduzione ai metodi Bayesiani in statistica applicata. Class website.
Learning Objectives
Knowledge acquired:
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.
Prerequisites
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.
Course program
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.