Computational Methods in Statistics

Objectives

This course unit is intended to provide students with the skills to use the R Project software, suitable for complex statistical analyzes and for the management of large databases.

The acquisition of these competences is fundamental to the UCs that follow during the course.

General characterization

Code

12226

Credits

3.0

Responsible teacher

Gracinda Rita Diogo Guerreiro

Hours

Weekly - 2

Total - 38

Teaching language

Português

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 with two practical assignments (one individual and one in group) to be carried out during the semester.

-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 attend the course in 2020/2021, the student must attend, at least, at 2/3 of the PRESENCIAL classes.

 

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:

 - 1 Test (T) and one Practical Assignment (PA) to be carried out during the semester. The Test will be individual, lasting 90 minutes, to be solved online in Moodle. PA is a computational group assignment, to be solved out of class.

Failure to appear on an evaluation results in a rating of 0 in that assessment.

 

Normal Season Approval

Considering NT and NPA the grades obtained in the Test and in the Practical Assignment, respectively, it is considered that a student obtains approval in the course unit if simultaneously verifies the following conditions:

 - Classification in T: T> = 7,5 values.
 - Note Normal Season: EN = 0,67T + 0,3 PA> = 9,5 values.


Approval at Time of Appeal

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

The evaluation will consist of an individual Computational Practical Exam, to be held in a Computational Laboratory, lasting 3 hours.

The Assessment Note, NER, will be determined by:

 NER=max(NER , 0.7xNER+0.3xTP)


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 to carry out a complementary test for grade defense.

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: