To introduce students to Statistics, both descriptive and inferential.

Knowledge and understanding: understand the fundamental principles of Probability, Statistics as a tool for solving real problems ; understand in which way methods are developed and in which way they are analyzed.

Applying knowledge and understanding: be able to apply methods to real problems along with implementations in some programming language.

Making judgements: be able to recognize the strengths and weaknesses of the methods.

Communication: know how to expose the resolution of a problem by using a suitable procedure.

Lifelong learning skills: know how to gather information from the web or textbooks in order to solve problems posed in real-life situations.

Basics of calculus and linear algebra.

Descriptive Statistics, Probability, discrete random variables, continuous random variables, inferential Statistics: sampling statistics, estimation, testing Statistical Hypotheses.

1) Lecture notes.

2) Sheldon Ross. Introductory Statistics, Third Edition, Elsevier.

Introduction: the collection of data; the description of the data;
drawing conclusion from the data; population and samples.

Descriptive statistics, describing the data: frequency tables; pie charts;
grouped data values and histograms; stem-and-leaf plots; paried data.

Descriptive statistics, summarizing the data: mean; median; percentiles; mode; variance;
standard deviation; interquartile range; box-plot; normal data; correlation coefficient;
causation and association.

Probability: experiment; sample space; events; measure of probability; probability
for experiments with discrete sample space; probability for experiments with continuous
sample space; conditional probability; independence of events; the Bernoulli process.

Discrete random variables: discrete random variables; probability mass
functions; binomial random variables; indipendence of random variables;
mean of a random variable; variance of a random variable;
mean and variance of a binomial random variable.

Continuous random variable: continuous random variables, probability density
functions; normal random variables; independence; mean and variance for Continuous
random Variables; finding probabilities for normal random variables; properties of normal
random variables; percentiles.

Sampling Statistics: the sample mean; the central limit theorem; sampling proportions;
the sample variance.

Estimation: estimating the mean; estimating the mean when the standard deviation is
unknown.

Testing Statistical Hypotheses: statistical hypothesis; tests for normal distributions with known variance;
tests for normal distributions with unknow variance.

Frontal lessons with slides (theory), blackboard (exercises) and computer (MATLAB).

The exam consists in a written part with three exercises and, after a positive evaluation of this part, a second oral part.

This course explores topics closely related to one or more goals of the United Nations 2030 Agenda for Sustainable Development (SDGs).