MATHEMATICAL METHODS AND MODELS FOR ENVIRONMENT

Course objectives

Skills to be developed and expected learning outcomes: Specific objectives: acquire knowledge about deterministic and probabilistic population models and descriptive and inferential statistical tools useful for environmental analysis; develop their own judgment by developing a real or simulated data analysis paper.

Channel 1
DARIO BENEDETTO Lecturers' profile

Program - Frequency - Exams

Course program
Lesson diary on http://brazil.mat.uniroma1.it/dario#didattica Introduction to dynamical systems: difference equations and differential equations. Descriptive statistics; Absolute and relative frequencies, graphical representations. Mean and standard deviation; medians and percentiles. Bivariate and multivariate statistics. Correlation, linear regression; principal components. Regression for exponential and power laws. Elements of probability. Independence. Bayes' formula, the law of large numbers. Random variables. The main probability distributions in modeling. Inferential statistics. Parameter estimation of a distribution, confidence intervals. Hypothesis testing. Introduction to various statistical tests.
Prerequisites
Basic knowledge of differential calculus, probability and elementary statistics, provided by the courses of the first year of the three-year degree.
Books
teacher's notes on http://brazil.mat.uniroma1.it/dario/#didattica Benedetto, Degli Esposti, Maffei: "Matematica per le scienze della vita",III edizione G. Gaeta: "Modelli matematici in biologia", Springer Whitlock, Schluter: Analisi statistica dei dati biologici, Zanichelli 2010 Iacus, Masarotto: "Laboratorio di statistica con R"
Frequency
Suggested.
Exam mode
The exam aims to evaluate learning through dissertation and oral test. The dissertation must relate to a data analysis or simulation experience, chosen by the student, and is used to verify the acquisition of mathematical methods for simulating and / or analyzing data and related IT tools. It can be replaced by carrying out exercises. The oral test serves to verify the understanding of mathematical models and consists of a discussion on the most relevant topics. To achieve a score it seems to 25/30, the student must demonstrate to have acquired a good knowledge and competence on basic topics of the course. To achieve a score of 30/30 cum laude, the student must demonstrate that he has acquired excellent competence and knowledge of all the topics covered during the course.
Lesson mode
Lectures 32 hours, exercises 20 hours.
  • Lesson code1047954
  • Academic year2024/2025
  • CourseEnvironmental Monitoring and Recovery
  • CurriculumSingle curriculum
  • Year2nd year
  • Semester1st semester
  • SSDMAT/07
  • CFU6
  • Subject areaDiscipline agrarie, tecniche e gestionali