72534 - Numerical Methods of Statistics

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

At the end of the course the student has acquired: -the knowledge of software instruments for the data analysis - the definition and the main characteristics of continuous and discrete distributions with their moments - some numercial methods for linear regression and its application in economy - the Page Rank algorithm for data mining.

Course contents

 Definitions and examples on probability. Discrete and continuous distributions. Descriptive statistics. Points and interval estimations.  Hypothesis test. Least squares data approximation: linear regression, polynomial functions of higher order and nonlinear least squares. Numerical methods for the solution of the discrete linear least squares problem.
Simulations and programming environment R. Principal functions for graphics and  data analysis. Guided exercises on examples with simulated and real data.

 

Introduction to statistical learning. Classification task.


Readings/Bibliography

1) E. Loli Piccolomini, A. Messina, Statistica e calcolo con R, McGraw-Hill Education, settembre 2015
2)J. K. Kerns, Introduction to probability and statistics using R, download from CRAN

3) Casella, Fienberg, Olkin, An introduction to Statistical learning with a applications in R, Springer.

 

Teaching methods

Lessons and guided exercises on laptop.The exercises consist in small programs in the R environment on the main topics dealed with in the lessons.

Assessment methods

The student must deliver a project assigned during the lessons. the exam is written.

Teaching tools

Example program files given by the teacher.

Office hours

See the website of Elena Loli Piccolomini