Computational Methods in Statistics

General characterization

Prerequisites

There are no requirements.

The Course Unit is accessible to any student in the 1st cycle of studies.

Bibliography

Dalgaard, P. (2008), Introductory Statistics with R, Springer-Verlag, New York

Everitt, B.S., Hothorn, T. (2010), A Handbook of Statistical Analysis using R, CRC Press, Chapman & Hall

Figueiredo, F., Figueiredo, A., Ramos, A., Teles, P. (2009), Estatística Descritiva e Probabilidades– Problemas resolvidos e propostos com aplicações em R, Escolar Editora.

Venables, W.N., Smith, D.M., R Core Team, (2018), An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics, CRAN.R-Project

Teaching method

The teaching method used in this course unit can be summarized as follows:

- In a first strand, the themes are introduced through an oral presentation, whose main objective is to motivate in the student the interest in studying this subject, while drawing attention to the important aspects of the subject under study. The oral presentation is traditionally done on the board with support of "slides".

- In a second slope are proposed and corrected computational exercises and doubts are made.

- In a third area, a continuous evaluation is carried out practical assignments .

-The fourth aspect is the practical classes, which are aimed at the practical preparation of students regarding R Project software.

Evaluation method

EVALUATION RULES

Frequency 

In order to obtain frequency in the course the student must perform the practical assignment.

 

Evaluation

Obtaining a frequency is compulsory for the purposes of the assessment tests.

The evaluation of knowledge of the curricular unit of Computational Methods in Statistics is constituted by 2 evaluation elements:

- 2 Tests (T1 and T2), a Practical Assignement (TP), to be carried out during the semester. The tests are individual and are taken in person. The PT will be a computational group assignment, to be solved outside of class. Work is discussed in groups, with different marks for each member of the group.

Failure to attend an assessment will result in a mark of 0 for that assessment.

Normal Season Approval

Considering T1, T2, TP the marks obtained in the Tests and Practical Work, respectively, a student is considered to have approved the course if they simultaneously fulfil the following conditions:

- Test grade: T1 >= 7.0.
- Normal Season Grade: EN = 0.35 T1 + 0.4 T2 + 0.25 TP >= 9.5.

Approval at Appeal Season

The Assessment of Appeal Season may be presented to any student who has obtained a course attendance.

The evaluation will consist of an individual Computational Practical Exam (ER).

The Exam Note in Appeal Season (NER), will be determined by:

NER=max(ER ; 0,75 x ER + 0,25x TP), com ER >=7.0

Grades Improvements and Defenses

The student wishing to improve the grade must register for this purpose at the Academic Office. 

If the student obtains a final classification superior to 18 values, he can choose between remain with the classification of 18 values ​​or carrying out a complimentary test for grade defence.

Subject matter

1 Introduction to R
1.1 What is R
1.2 Installing the R
1.3 R Help and Documentation
1.4 The Packages of the R
1.5 Objects in R
1.6 Import and Export of Data in R
1.7 Data manipulation in R
1.8 Programming in R

2 Descriptive Statistics with R
2.1 Absolute and Relative Frequency Tables
2.2 Measures of Location and Dispersion
2.3 Asymmetric Measures
2.4 Flaring Measures
2.5 Outliers Detection

3 Graphical Data Analysis
3.1 Dispersion Graphs
3.2 Bar Graphs
3.3 Circular Diagrams
3.4 Box-of-Mustache Diagrams
3.5 Stem-and-Leaf Diagrams
3.6 Histograms
3.7 Frequency Polygons

4 Calculation of Probabilities with R
4.1 Combinatorial Calculation
4.2 Laplace rule
4.3 The Binomial Distribution
4.4 The Normal Distribution

5 Basic Statistical Analysis of Datasets

Programs

Programs where the course is taught: