# 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

Maria de Lourdes Belchior Afonso

##### Hours

Weekly - 2

Total - 38

##### Teaching language

Português

### Prerequisites

Available soon

### 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

1 themes are introduced through an oral presentation, calling attention to the important aspects of the subject being studied.

2 computational exercises are proposed and corrected (plus tutorial classes).

3 a continuous evaluation is carried out with two practical assignments.

4 practical classes (R Project software).

### Evaluation method

**Frequency **

In order to attend the course, the student must:

i. has not missed more than three of the classes taught, if you are enrolled for the first time in the discipline,

ii. has attended at least 2/3 of the classes given, if already enrolled in the course.

All students with a Special Status recognized by the FCT NOVA''s general evaluation rules are exempt from attendance.

**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 (TP) to be carried out during the semester. The Test will be individual, lasting 90 minutes, to be solved in a computer lab. TP will be a computational group assignment, to be solved out of class.

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

**Normal Season Approval**

Considering NT and NTP 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 TP: TP> = 7,5 values.

- Note Normal Season: EN = 0,6 T + 0,4 TP> = 9,5 values.

**Approval at Time of Appeal**

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

A Resource Review will consist of an individual Computational Practical Exam, to be held in a Computational Laboratory, lasting 3 hours.

The Resource Assessment Note, NER, will determine the student''s Resource Note.

**Grades Improvements and Defenses**

The student wishing to take the mark improvement exam must register for this purpose at the Academic Office. Note Improvement can be made at Appeal Time. In order to improve the grade, it is required that:

The mark obtained in this exam, NEM, is not less than 9,5;

The Improvement Note is higher than the mark already awarded to the student.

If the student obtains a final classification superior to 18 values, he / she can choose between being with the classification of 18 values or to carry out a complementary test for defense of note.

## 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