Statistics I

Code:

TGI37

Sigla:

EST I

Áreas Científicas
Classificação	Área Científica
OFICIAL	Matemática

Ocorrência: 2023/2024 - 2T

Ativa?	Yes
Unidade Responsável:	Departamento de Matemática
Curso/CE Responsável:	Industrial Management and Techinology

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
TGI	66	Plano de Estudos 2016	2	-	4	44	108

Docência - Responsabilidades

Docente	Responsabilidade
José António da Conceição Palma
José António da Conceição Palma

Docência - Horas

Theorethical and Practical :	2,00
E-Learning:	2,00

Type	Docente	Turmas	Horas
Theorethical and Practical	Totais	2	4,00
Theorethical and Practical	José António da Conceição Palma		4,00
E-Learning	Totais	1	2,00
E-Learning	José António da Conceição Palma		2,00

Língua de trabalho

Portuguese

Objetivos

• Provide basic training on statistical learning techniques and theory.

• Identify the main theoretical models studied.

• Construct and interpret confidence intervals.

• Construct and interpret parametric hypothesis tests.

• Estimate and interpret linear regression model parameters.

• Apply statistical inference techniques as a decision support tool and interpret the results obtained.

• Analyze, evaluate, interpret and critically defend the results obtained.

Resultados de aprendizagem e competências

This Curricular Unit is intended to acquire and apply knowledge of Statistics, so that the student can use basic tools in this area, such as techniques and methodologies in data processing, and that allow the student to develop the capacity for analysis and reasoning to certain phenomena, namely in the field of Technology and Industrial Management.

Modo de trabalho

Presencial

Pré-requisitos (conhecimentos prévios) e co-requisitos (conhecimentos simultâneos)

Basic knowledge at the level of 12th grade mathematics and basic notions of integral calculus.

Programa

1. Descriptive Statistics

Qualitative and quantitative variables. Frequency tables and graphic representations. Location and dispersion measures.

2. Random Variables

Notion of random variable. Probability and density functions of discrete and continuous random variables, respectively. Distribution function. Location and dispersion parameters: expected value, variance and standard deviation; characterization and properties.

3. Theoretical Distributions

Discrete Theoretical Distributions: Binomial Distribution and Poisson Distribution; characterization, parameters and properties. Continuous Theoretical Distributions: Exponential Distribution and Normal Distribution; characterization, parameters and properties. Brief reference to the t-Student distribution.

4. Simple Linear Regression

Simple linear regression model: estimation, model validation and inference.

Bibliografia Obrigatória

Reis, E.; e outros; , Estatística Aplicada, Edições Sílabo, 2003

Bibliografia Complementar

Graça, M. E..; Introdução às Probabilidades e Estatística, FCUL, Sociedade Portuguesa de Estatística, 1998
Galvão de Melo, F.; Probabilidades e Estatística, Escolar Editora, 1993

Métodos de ensino e atividades de aprendizagem

In theoretical and practical classes, the expository method and problem solving are combined.

Remotely on the Moodle platform and on the Microsoft Teams platform, they access the UC contents through various materials and are proposed to carry out weekly activities.

Tipo de avaliação

Distributed evaluation with final exam

Componentes de Avaliação

Designation	Peso (%)
Teste	60,00
Trabalho escrito	40,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Estudo autónomo	86,00
Frequência das aulas	22,00
Total:	108,00

Obtenção de frequência

The use of this curricular unit can be obtained through two assessment processes: Continuous Assessment or Assessment by Exams.

Fórmula de cálculo da classificação final

The Continuous Assessment presupposes the completion of 1 test and 1 group work.

Designating the classification (from zero to 20, rounded to the nearest hundredths) obtained in the test by T and the group work grade by NTG, the final classification CF (rounded to the nearest integer) will be calculated as follows:

CF=T * 0.60+0.4 NTG

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