Stefanini F.M., 2020, Introduzione ai metodi Bayesiani in statistica applicata (web site)
Learning Objectives
Knowledge acquired:
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.
Prerequisites
Courses to be used as requirements (required and/or recommended)
Courses required: none
Courses recommended: basic calculus.
Teaching Methods
Contact hours for:
Lectures:48 (webinars and classwork).
Type of Assessment
Oral exam on subjects of lectures, laboratory assignments and homework.
Course program
How to study for the final exam, the R software. Frequencies distributions, moments, quantiles. Graphical univariate and multivariate summaries.
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 variables.
Randomized controlled experiments: random sampling, randomization, control, replication, target and baselines variables.