Probabilità e Statistica per l’ingegneria e le scienze, S. M. Ross, Maggioli Editore, 2015 (terza edizione)
Learning Objectives
The aim of this course is to introduce students to probability theory and to the main parametric inference methods.
Prerequisites
Courses required: Analysis I: Integral and Differential Calculus.
Teaching Methods
Lectures and exercises sessions.
Further information
Additional teaching material will be provided during the course through the e-learning platform.
Type of Assessment
Written examination.
Course program
Descriptive statistics: frequency distributions, graphical representations, measures of central tendency and measures of variability.
Elements of probability theory: sample space and events, the algebra of events, axioms of probability, conditional probability.
Random variables: marginal distributions, joint distributions and conditional distributions, expectation and variance, some discrete distributions, some continuous distributions.
Sample statistics and Central Limit Theorem.
Parameter estimation: properties of estimators, Maximum Likelihood method, pivotal quantity method for obtaining confidence intervals, confidence intervals for the mean and for the variance of a Normal population, confidence intervals for the mean of a Bernoulli population.
Hypothesis testing: basic concepts for obtaining hypothesis tests, test for the mean and for the variance of a Normal population, test for the mean of a Bernoulli population.