Computational Statistics I
Cod: 22008
Department: DCET
ECTS: 10
Scientific area: Statistics
Total working hours: 260
Total contact time: 40
The statistical system R is one of the most flexible, powerful and professional currently existent to perform tasks in statistics from the most elementary to advanced ones. This project is developed and maintained by some of the most prestigious statisticians of today. Moreover, it has the advantage of being free and using processes of transfer and easy installation. The aim of this course is to foster in students the practical skills to enable, collect, process and analyze statistical information from observed realities. Based on the use of statistical software R, real data sets and / or simulated data will be used in order to illustrate the potential of the software in the statistical treatments and develop in students the necessary sensitivity for the collection, compilation and interpretation of available information.
R Language
Random variables
Distributions
Simulation
Upon completion of this learning unit, the student should be able to:
- Recognize the role and importance of computing to aid the processing and data analysis;
- Describe the R programming environment and its key features.
- Identify the main control structures of programming language used in R;
- Apply technical computing language R to solve problems involving random variables, statistical distributions, estimation and hypothesis testing, generation of numbers and random variables.
- Solve problems using the R program, involving the themes dealt with statistics.
1.Introduction to the R environment
2.Random Variables
3.Distributions of Probability
4.Introduction of simulation
5.Monte Carlo methods in Statistical Inference
• Dalgaard, Peter (2008): Introductory Statistics with R,  2nd edition, Springer,  ISBN: 978-0-387-79053-4.
• Verzani, J. (2005): Using R for Introductory Statistics, Chapman&Hall/CRC.
• Ross, Sheldon M. (2009): Introduction to Probability and Statistics for Engineers and Scientists, fourth edition, Elsevier/Academic Press, Burlington, MA.
• J. E. Gentle (2005): Random Number Generation and Monte Carlo Methods 2nd Edition, Springer. ISBN 0-387-0017-6 e-ISBN 0-387-21610
• Jones, O., Maillardet, R., Robinson, A. (2014): Introduction to Scientific Programming and Simulation using R, Second Edition. Chapman and Hall / CRC, The R Series. International Standard Book Number-13: 978-1-4665-7001-6 (eBook-PDF)

E-learning (fully online).

Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.
Additional support materials will be available on e-learning platform.