THREE-DIMENSIONAL MODELING

Course objectives

GENERAL OBJECTIVES The course aims to provide students with a basic understanding of concepts and tools relevant in managing uncertainty through Probability and Statistics. Specifically, the course aims to help students understand: events and the Mathematical approach via sets; random variables and random elementary models; transformations of random variables and random simple models; independence and a gentle introduction to the analysis of dependence; limit theorems with applications to the sample size; basics on descriptive statistics; the linear model; parametric estimation. SPECIFIC OBJECTIVES KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Probability and Statistics. Students will learn to recognize and master random models and apply them in real-world contexts for data analysis. APPLICATIVE SKILLS. Thanks to the course, students will be able to critically classify and analyse data in order to build a model via parameters estimation. JUDGMENT AUTONOMY. The course will empower students to choose and properly characterize models via data. COMMUNICATION SKILLS. By the end of the course, students will be able to illustrate models and data with reporting processes understandable to professionals. LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of probabilistic and statistical methodologies.

Channel 1
ROBERTA VARRIALE Lecturers' profile

Program - Frequency - Exams

Course program
Descriptive statistics (measures of central tendency and dispersion; graphical representations of data; analysis of univariate and bivariate distributions) Inference (point parameter estimation, interval parameter estimation, testing of parametric hypotheses) Regression model
Books
Sheldon M. Ross "Probabilità e statistica per l'ingegneria e le scienze", Edizione Apogeo, Milano, 2003.
Channel 2
ROBERTA VARRIALE Lecturers' profile

Program - Frequency - Exams

Course program
Descriptive statistics (measures of central tendency and dispersion; graphical representations of data; analysis of univariate and bivariate distributions) Inference (point parameter estimation, interval parameter estimation, testing of parametric hypotheses) Regression model
Books
Sheldon M. Ross "Probabilità e statistica per l'ingegneria e le scienze", Edizione Apogeo, Milano, 2003.
  • Academic year2025/2026
  • CourseManagement Engineering
  • CurriculumSingle curriculum
  • Year1st year
  • Semester2nd semester
  • SSDSECS-S/01
  • CFU3