# ELABORAZIONE DATI ED ELEMENTI DI STATISTICA

Degree course:
Corso di First cycle degree in ENGINEERING FOR WORK AND ENVIRONMENT SAFETY
Academic year when starting the degree:
2016/2017
Year:
2
Academic year in which the course will be held:
2017/2018
Course type:
Basic compulsory subjects
Credits:
6
Period:
First Semester
Standard lectures hours:
48
Detail of lecture’s hours:
Lesson (48 hours)
Requirements:

Linear Algebra and Calculus.

Final Examination:
Scritto e Orale Congiunti

Written and oral exam

Assessment:
Voto Finale

Basic knowledge for implementing on a computer algorithms of numerical and statistical type,
accompanied by the ability of reading and interpreting the results.

For the numerical part a goal is to acquire the tools for evaluating stability and complexity of the
considered methods.

For the statistical part a goal is the ability in visualizing, summarizing, and analyzing the data both in one and two dimensions.

PART A: Numerical treatment of Data

Basic matrix theory. Unitary, Hermitian, positive definite matrices. Elementary matrices (Gauss, Householder)

Numerical solution of linear systems: coefficient matrices in special form (unitary, triangular etc)

Numerical solution of linear systems: Gaussian elimination, pivoting, QR factorization

Iterative solvers: Jacobi, Gauss-Seidel
Evaluation of a polynomial at a point.

Interpolation. Least squares

PARTE B:

Statistical treatment of Data

Introduction to probability and to the statistical methodology. Introduction to the R freeware
statistical software.

Type of statistical variables and their graphical representations. Absolute and relative frequencies.

Measures of central tendency (mean, mode and median). Measure of dispersion (range, variance,
standard deviation, coefficient of variation, quantiles, interquantile range, notion of homogeneity).

Measures of shape (symmetry). Bivariate descriptive statistics: two-way tables, marginals and

joint bivariate distributions (discrete case). Joint graphical representations of two random variables.

Covariance and correlation coefficient. Linear regression: simple and multivariate. Discrete and
continuous random variables (Bernoulli and Gaussian).

“Scientific Computing with Matlab and Octave”, by A. Quarteroni, F. Saleri, Springer

“Metodi Numerici per l’Algebra Lineare”, by D. Bini, M. Capovani, O. Menchi, Zanichelli

“Metodi Numerici”, by R. Bevilacqua, D. Bini, M. Capovani, O. Menchi, Zanichelli

“Statistica”, di Levine D. M, Krehbiel T. C. and Berenson M. L. (2010), 5-th Ed, Pearson, Prentice Hall

or, alternatively

“Statistica”, by Newbold, P., Carlson, W., Thorne B.M., Pearson

Software R online: “An introduction to R” https://cran.r-project.org/manuals.html and notes given by the Professor

Classroom teaching; practical exercises (on blackboard and on the computer)

for discussing with the professors, please use email: antonietta.mira@uninsubria.it; stefano.serrac@uninsubria.it