### Teachings

Select Academic Year:     2017/2018 2018/2019 2019/2020 2020/2021 2021/2022 2022/2023
Professor
DANIELA LERA (Tit.)
SILVIA COLUMBU
Period
Second Semester
Teaching style
Convenzionale
Lingua Insegnamento
ITALIANO

Informazioni aggiuntive

Course Curriculum CFU Length(h)
[60/64]  MATHEMATICS [64/00 - Ord. 2017]  PERCORSO COMUNE 3 36

### Objectives

The student will learn the principal functions of the Matlab software that will allow him to handle mathematical objects such as vectors and matrices. The student will also learn how to build graphs in 2 and 3 dimensions. Furthermore, will be introduced the symbolic calculation toolbox of Matlab which will allow to the student to solve simple problems of linear algebra and analytical geometry.

The student will be able to perform a descriptive statistical analysis of real data using the statistical software R. At the same time he will have acquired a good knowledge of the basic resources of the software R.

### Prerequisites

Mathematical Analysis 1, Algebra 1, Geometry 1

### Contents

The course includes two modules of 2 CFU and 1 CFU (24 and 12 hours):
1. Introduction to the scientific Matlab environment. Constants and variables. Real and complex arithmetic. Use of the library functions. Manipulation of vectors and matrices. Construction of graphics in 2 and 3 dimensions. Programming through scripts and functions. Basic programming with Matlab.
3. Basics of symbolic tool:
Simplifications of algebraic expressions; solution of equations and linear systems; calculation of limits and derivatives; calculation of integrals.
Linear algebra: vector and matrix calculus. Determinant, inverse, eigenvalues and eigenvectors. Linear systems resolution.
2. Introduction to the statistical environment R. Main features and interactive use of the environment. The help function: learn to use the functions implemented in the software by reading the available documentation. Data manipulations using the main objects of R: construction of numerical vectors, of character and logic type, construction of matrices, management of missing data, generation of subsets of matrices and vectors, arithmetic calculation exploiting the functionality of the software. Data entry and creation of a dataset. Lists on R. Saving data externally, importing data and coding variables. Univariate descriptive statistics: position indexes, variability indices, form of the distribution of a variable, frequency tables. Bivariate descriptive statistics: double-entry frequency tables, correlation measures, relationship between a categorical variable and a numerical one, relationship between two categorical variables, introduction to linear regression. Elementary graphic tools: bar charts, pie charts, histograms, boxplots, scatterplots, bivariate bar charts. Basic programming elements: construction of a function, use of cycles and conditional functions.

### Teaching Methods

Exercises in laboratory supported by some theoretical explanation and recall on the blackboard and/or slides.

### Verification of learning

The final test consists in solving, in the laboratory, 2 exercises (one per module) to be solved with the aid of the 2 softwares.

### Texts

Manuals of Matlab and of R made available by the teachers on their reference web sites.
Other material: slides from the lessons held in the classroom.
The material is available at the links:

https://people.unica.it/danielalera/didattica/materiale-didattico/