Statistics and Multivariate Analysis in R: applications in multicomponent systems

Recommended Prerequisites

Not applicable.

Teaching Methods

The proposed course in e-learning, requires assiduous work and some self-learning. The contact will be based on the resources available on the internet.

The evaluation method arises from the interaction of the studant with the training team: problems will be presented and requested their responses.

Final information to be published will be Satisfactory, Good, Very Good and Excellent.

Learning Outcomes

It is intended that students acquire knowledge in the use of computational tools for statistical and multivariate data analysis, in a comprehensive and current perspective of methods implemented in R environment, developing a self-critical analysis and interpretation of the results from specific and pratical examples.

Work Placement(s)

No

Syllabus

  
P1-Introduction to R and its programming environment
(i)Why use R?
(ii)Obtaining and installing R
(iii)Working with R
(iv)Packages
(v)Applications(base functions,structure and data manipulation,statistical analysis,ata inspection and visualization).
P2-Multivariate Statistics
(i)Introduction: Data structures.Classification of multivariate techniques.
(ii)Similarity and groups.
(iii)Variance,dimensionality reduction and data analysis.
(iv)Multivariate statistics applied to real multicomponent systems.

Head Lecturer(s)

Jorge Luís Gabriel Ferreira da Silva Costa Pereira

Assessment Methods

Assessement
The evaluation method arises from the interaction of the studant with the training team: problems will be presented and requested their responses. Final information to be published will be Satisfactory, Good, Very Good and Excellent.: 100.0%

Bibliography

M. J. Crawley, The R Book, 2nd ed, Wiley, Imperial College London at Silwood Park, UK http://www.bio.ic.ac.uk/research/mjcraw/therbook/index.htm, 2013

R. I. Kabacoff, R in Action: Data analysis and graphics with R, MANNING Shelter Island, 2011.

A. F. Zuur, E. N. Ieno, E. H.W.G. Meesters, A Beginner’s Guide to R, Springer, 2009. 

R. Wehrens, Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences, Springer, 2011.

B. Everitt,T. Hothorn, An Introduction to Applied Multivariate Analysis with R, Springer, 2011.

W. N. Venables, D. M. Smith and the R Core Team, An Introduction to R (Notes on R: A Programming Environment for Data Analysis and Graphics),

disponível em: http://cran.r-project.org/doc/manuals/R-intro.pdf

 

J H Maindonald, Using R for Data Analysis and Graphics Introduction, Code and Commentary

disponível em: http://cran.r-project.org/doc/contrib/usingR.pdf

 

Robert I. Kabacoff, R IN ACTION: Data analysis and graphics with R

disponível em: http://m.friendfeed-media.com/36d8ab666d485a984e441fd9d0f606c8c8553061