The course provides basic knowledge on statistics and probability theory.

Ability to solve applied problems involving basic probability theory and to analyze data with simple tools from statistics.

Calculus I and II.

Probability. Introduction to probability and counting. Axioms of probability. Conditional probability and Bayes theorem. Discrete random variables: binomial and Poisson distributions. Expectation and distribution parameters. Continuous random variables: Gamma and Normal distributions. Joint distributions: covariance and correlation, linear regression.
Statistics. Point estimation. Central limit theorem. Statistical intervals based on a single sample. Inferences on the mean and on variance. Testing hypotheses.

Problem sessions will widen and deepen the topics treated in the lectures. Time will be also devoted to problem solving and working out examples and exercises.

Main text:
J.S. Milton, J.C. Arnold
Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, 4th edition
Mc Graw Hill international editions
Problem solving and exercises:
J.S. Milton, J.C. Arnold
Student Solutions Manual to accompany Introduction to Probability and Statistics, 4th Edition
Mc Graw Hill international editions

The exam will consist in a test requiring the solution of three or four exercises and in an oral discussion on the solution strategy used in the test. The written test will account for roughly 80% of the final grade.