Numerical Methods of Statistics 2017/2018

Academic Year 2017/2018

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 and polynomial functions of higher order. 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

Written and oral exam with the discussion of the exercises proposed by the teacher during the course.

72534 - Numerical Methods of Statistics

Course Unit Page