Main aims of Statistics. Experimental and observational studies. Finite and infinite populations. Descriptive statistics. Introduction to probability. Random variables. Models for random variables. Sampling statistics distributions. Parameters estimation. Confidence intervals and hypotheses testing.
Sheldon M. Ross. Probabilità e Statistica per l’ingegneria e le Scienze, 2015 Maggioli Editore
Learning Objectives
Knowledge. Basic understanding of statistical inference, probability calculus, and their relations Expertise. Students will be trained to solve simple problems involving the treatment of sampling data, to verify hypoteses to make simple predictions.
Teaching Methods
Oral lectures and sessions of exercises
Type of Assessment
Written and oral examination
Course program
Introduction. Population and samples. Graphical representations and layout. Chebychev inequality. Probability elements and axioms. Conditional probability. Bayes theorem. Independence. Introduction to random variables. Specific random variables: bernoulli, binomiale, Poisson, hypergeometric, uniform, Gaussian,exponential, Gamma,Chi-square,t,F,logistic. Sampling statistical distributions. Sampling mean and Central limit theorem. Sampling variance. Parameter estimation. Maximum likelihood estimators. Confidence intervals. Prediction intervals. Estimators and Bayesian estimators. Hypotheses testing. Significance level. Hypotheses testing for a single or two population. Hypotheses testing for a proportion and for the parameters of two population distributed according to a Poisson.