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Segurança e Proteção Civil

Quantitative Methods

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Publication in the Diário da República: Despacho n.º 10344/2023 de 09/10/2023

4 ECTS; 1º Ano, 1º Semestre, 35,0 TP , Cód. 62231.

Lecturer
- Eugénio Manuel Carvalho Pina de Almeida (1)(2)

(1) Docente Responsável
(2) Docente que lecciona

Prerequisites
NA

Objectives
O1 - Development of a critical spirit that enables the understanding, interpretation and application of knowledge in the field of mathematical analysis and statistics to the area of Civil Protection;
O2 - Apply logical reasoning to concrete problems, using statistical tools;
O3 - Knowledge and development of skills for the analysis, presentation and visualization of data in the area of security and civil protection.

Program
1. Brief Notions of Analysis in R
1.1 Sets of Numbers
1.1.1 Sets of NATURAL Numbers;
1.1.2 Integer Number Sets;
1.1.3 Sets of RATIONAL Numbers;
1.1.4 Sets of REAL Numbers;
1.2 Operations between numbers and their properties
1.2.1 Commutative, associative and distributive properties;
1.2.2 Rules of signs, potentiation and exponentiation
1.3 Concept of real function of real variable (RFRV).
1.3.1 Study of the affine function, quadratic function and exponential function.
1.3.2 Graphical representation of functions.
2 Basic notions of statistics
2.1 Distinction between population and sample
2.2 Sampling
2.3 Statistical unit and statistical data
2.4 Classifying data according to its nature
2.5 Methodology for solving a statistical problem
2.6 Proposed exercises
3. Descriptive Statistics
3.1 Forms of tabular and graphical representation
3.1.1 Frequency table for univariate data
3.1.1.1 Discrete qualitative or quantitative data
3.1.1.2 Continuous quantitative data
3.1.2 Graphical representation of univariate data
3.1.3 Contingency table for bivariate data
3.1.4 Graphical representation of bivariate data
3.1.5 Exercises
3.2 Descriptive measures
3.2.1 Measures of location
3.2.1.1 Central tendency
3.2.1.1.1 Arithmetic mean
3.2.1.1.2 Mode
3.2.1.1.3 Median
3.2.1.1.4 Comparing the mean, median and mode
3.2.1.2 Exercises
3.2.1.2 Non-central tendency
3.2.1.2.1 Quantiles
3.2.2 Measures of dispersion
3.2.2.1 Absolute measures
3.2.2.1.1 Total amplitude
3.2.2.1.2 Interquartile range
3.2.2.1.3 Mean absolute deviation
3.2.2.1.4 Variance
3.2.2.1.5 Standard deviation
3.2.2.2 Relative measures
3.3 Practical application of descriptive measures to Civil Protection
4. Applying Power Business Intelegence (Power BI) to data visualization and interpretation
4.1 Data transformation
4.2 Error correction
4.3 Creating and interpolating columns
4.4 Data models
4.5 Visual components - graphs, tables, maps, cards, etc.
4.6 Associating visual components with data
4.7 Formatting visual components
4.8 Composing and publishing reports
4.9 Creating dashboards and pdfs of reports
4.10 Viewing reports and dashboards on various types of devices.

Evaluation Methodology
Assessment methodologies:

Evaluation by Frequency (NFF): (at the end of the semester) weighted average between a written test, T1, and a practical work of application of knowledge T2, carried out in class.
The final grade will be:
NFF = 4/5*T1+1/5*T2;
Students are exempt from the exam if they have a final grade of 10 or more (9.5 with rounding).

Evaluation by Exam (NFE): a written test E1 including all the material taught in class.
The final grade will be obtained by the weighted average between the result of the exam, E1, and the result of the practical work applying knowledge T2, carried out during the semester. The final grade will be:
NFE = 4/5*E1+1/5*T2;
The student is exempt from the exam if he/she has a final mark of 10 or more (9.5 with rounding).

Bibliography
- Fernandes, R. (0). Rosa Brígida, conteúdos matemática e física. Acedido em 8 de julho de 2021 em https://doctrino.ipt.pt/course/view.php?id=4794

Teaching Method
1. Presential
M1: Theoretical classes
M2: Theoretical-practical classes
M3: Mentoring Guidance
M4: e-learning
2. Autonomous:
M6: consultation of resources on the internet
M7: Resolution of additional exercises

Software used in class
Microsoft Excel
Microsoft Power BI

 

 

 


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