Laboratorio di Statistica Computazionale

 

Computational Statistics Laboratory

 

Anno accademico 2018/2019

Codice attività didattica
MFN1622
Docente
Prof. Maria Teresa Giraudo (Titolare del corso)
Corso di studio
Laurea in Matematica
Anno
3° anno
Periodo didattico
Primo semestre
Tipologia
D.M. 270 TAF F - Altre attività
Crediti/Valenza
3
SSD attività didattica
MAT/06 - probabilita' e statistica matematica
Erogazione
Tradizionale
Lingua
Inglese
Frequenza
Facoltativa
Tipologia esame
Scritto
Prerequisiti
  • English
  • Italiano

It is recommendable to have passed the exam of the second year Probability and
Statistics course.

 
 

Obiettivi formativi

  • English
  • Italiano

In accordance with the educational goals of the degree program envisaged in the SUA-CdS file, the aim of the course is to introduce the students to the applications of the basic statistical pronciples and techniques they have acquired. This is done by employing real problems and data sets coming from different fields such as for instance Biology, Engineering, Finance, Demography, Epidemiology and by introducing the statistical software R (www.r-project.com) and its programming facilities.

 

Risultati dell'apprendimento attesi

 

  • English
  • Italiano

Knowledge and understanding

The course, starting from basic Statistics knowledge, allows the students to employ them in real applications broadening at the same time the computational and computer science skills. The teaching material is in English and thus favours the habit to read mathematical papers and books in the original language.

Applying Knowledge and understanding

The course shows to the students specific statistical methodologies to extract qualitative information from quantitative data. Moreover it allows them to use specific computer science instruments to get the possibile information also by means of some programming skills.

Making judgements

The students are lead to propose and to analyze statistical models for real situations arising in other fields and to use such models to facilitate their study. They can work in group but they are also able to work satisfactorily on their own.

Communication

The students become able to discuss with experts in other subjects about problems of moderate difficulty and they realize the possibility to statistically formalize real situations and to suitably formulate useful models in several contexts. They are able to employ the English language in the specific fields .

 

 

Programma

 

  • English
  • Italiano

Introduction to the applications of Statistics and to the use of statistical software R.

One-dimensional descriptive Statistics: main statistical indexes (sample mean, mode, median, sample variance, coefficient of variation, curtosis, skewness); graphical representations of sample data

Two-dimensional descriptive statistics: contingency tables, sample correlation.

Simulating a sample; inverse transform method.

Hypothesis testing: parametrical and not parametrical tests for one and for two samples; chi square test for independence.

Goodness of fit tests.

Correlation and regression.

One and two way analysis of variance.

 

 

Modalità di insegnamento

  • English
  • Italiano

This course is given through practical lessons in the computer room. The detailed program of the lessons will be available on the Moodle page of the course.

Attendance to lessons is not compulsory, but highly recommended due to the necessity of learning and employing specific computer science instruments.

 

Modalità di verifica dell'apprendimento

  • English
  • Italiano

(1) First two exam dates: Students will be asked to complete during the course two assigned individual works. The works will be given a score covering 60% of the final grade as a whole The final exam consists of an exercise to solve with the software in the computer room (1 hour). It will be given 40% of the final grade.

(2) Other exam dates: The examination will take place in the computer room; students will be asked to solve two exercvise covering all aspects of the program (2 hours). The final grade will be determind solely by this exam.

 

 

Testi consigliati e bibliografia

  • Italiano
  • English

1) P. Dalgaard Introductory Statistics with R, Springer 2008

2) Owen Jones, Robert Maillardet, Andrew Robinson Introduction to Scientific Programming and Simulation Using R, Second Edition, Chapman and Hall/CRC 2014

3) Materiale didattico utilizzato a lezione presente sulla pagina del corso e sitografia segnalata dal docente.

 

 
Registrazione
  • Aperta
     
    Ultimo aggiornamento: 16/07/2018 15:14
    Campusnet Unito

    Location: https://www.matematica.unito.it/robots.html
    Non cliccare qui!