72534 - Numerical Methods of Statistics

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.

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

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

It is mandatory to complete the  homework assigned during the Laboratory to pass the exam.

The exam consists in a Laboratory  test and an oral discussion of the homeworks.

The final score is the sum of:

• the score of the Laboratory test (max. 27/30)
• the score of the homework (max. 5/30)

If the final score is greater than 30, the laude is assigned.

Teaching tools

Example program files given by the teacher.

Office hours

See the website of Elena Loli Piccolomini