Stefanini F.M., 20209, Introduzione ai metodi Bayesiani in statistica applicata: materiale ausiliario (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 classroom)
Further information
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.