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# Statistical Methods

 Code: EIC0105 Acronym: MEST

Keywords
Classification Keyword
OFICIAL Mathematics

## Instance: 2018/2019 - 2S

 Active? Yes Responsible unit: Department of Industrial Engineering and Management Course/CS Responsible: Master in Informatics and Computing Engineering

### Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIEIC 215 Syllabus since 2009/2010 1 - 4,5 56 121,5

### Teaching Staff - Responsibilities

Teacher Responsibility
António Miguel da Fonseca Fernandes Gomes

### Teaching - Hours

 Lectures: 2,00 Recitations: 2,00
Type Teacher Classes Hour
Lectures Totals 1 2,00
António Miguel da Fonseca Fernandes Gomes 1,50
Carlos Manuel Milheiro de Oliveira Pinto Soares 0,50
Recitations Totals 8 16,00
Luis Filipe da Silva Magalhães Dias 4,00
Sofia Cristina Guedes de Sousa e Cruz Gomes 4,00
António Miguel da Fonseca Fernandes Gomes 4,00
Vítor Manuel Araújo Cerqueira 4,00
Last updated on 2019-02-05.

Fields changed: Calculation formula of final grade, Componentes de Avaliação e Ocupação, Melhoria de classificação

Portuguese

### Objectives

This course unit aims to provide students with an integrated vision of the basic concepts and techniques of Statistics.

### Learning outcomes and competences

At the end of this course unit, students should be capable of:

-using methods to explore, summarize and present data;

- using statistical inference methods.

Presencial

### Program

1. INTRODUCTION TO STATISTICS: Data and Observations. Populations and Samples. Statistical Method.

2. DESCRIPTIVE STATISTICS: Types of Data and Measure Scales. Summarizing Categorical, Quantitative and Bivariate Data.

3. PROBABILITIES: Random Experiments. Sampling Spaces and Events. Probability, Conditional Probability and Independence. Total Probability and Bayes Theorem.

4. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Random Variables. Discrete and Continous Random Variables. Mass, Density and Cumulative Probability Functions. Population Parameters. Joint Probability Distributions. Covariance and Correlation. Transformed Variables.

5. MAIN DISCRETE AND CONTINUOS DISTRIBUTIONS: Binomial, Negative Binomial, Hypergeometric and Poisson Distributions. Uniform, Exponential and Normal Distributions. Chi-square, t and F Distributions.

6. SAMPLING AND SAMPLING DISTRIBUTIONS: Sampling and Random Sampling. Sampling Distributions. Central Limit Theorem. Generation of Random Variables.

7. ESTIMATION AND CONFIDENCE INTERVALS: Estimators and Estimates. Confidence Interval. Confidence Intervals for Expected Values, Variances and Proportions. Sample Size Determination.

8. STATISTICAL HYPOTHESIS TESTING: Statistical Inference Logic and Scope. Hypothesis Testing Methodology. Significance Level and Statistical Power (Type I and Type II Errors). Relationship between Hypothesis Testing and Confidence Intervals. Hypothesis Testing concerning Expected Values, Variances and Proportions.

9. INTRODUCTION TO REGRESSION ANALYSIS: Simple Linear Regression Model. Regressin Parameters Estimation (OLS). Inferences about regression Parameters. Predictions based on the Simple Linear Regression Model. Regression Assumptions.

10. INTRODUCTION TO DATA MINING: Main Concepts and Applications. Data Analysis with RapidMiner.

### Mandatory literature

A. Miguel Gomes e José F. Oliveira; Estatística - Apontamentos de Apoio às Aulas, 2018
Rui Campos Guimarães e José António Sarsfield Cabral; Estatística, 2ª edição, Verlag Dashofer, 2011. ISBN: 978-989--642-108-3

### Complementary Bibliography

Devore Jay L.; Modern mathematical statistics with applications. ISBN: 978-1-4614-0390-6
Nathan Tintle, Beth L. Chance, George W. Cobb, Allan J. Rossman, Soma Roy, Todd Swanson, Jill VanderStoep; Introduction to Statistical Investigations, Wiley, 2015. ISBN: 978-1-119-15430-3
Wonnacott Thomas H. 1935-; Introductory statistics. ISBN: 0-471-51733-X

### Teaching methods and learning activities

Theoretical classes: presentation of the course unit themes followed by examples and problem solving Theoretical-practical classes: problem solving and clarification of doubts

RapidMiner

### keywords

Physical sciences > Mathematics > Probability theory
Physical sciences > Mathematics > Statistics

### Evaluation Type

Distributed evaluation with final exam

### Assessment Components

Designation Weight (%)
Exame 70,00
Participação presencial 0,00
Teste 20,00
Trabalho prático ou de projeto 10,00
Total: 100,00

### Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 50,00
Frequência das aulas 56,00
Trabalho laboratorial 22,00
Total: 128,00

### Eligibility for exams

Admission criteria set according to Article 4 of General Evaluation Rules of FEUP.

### Calculation formula of final grade

The final mark (CF) will be obtained by the following formula:
CF = 0.30 AD + 0.70 EF

AD = 2/3 FA + 1/3 TG

- 6 quizzes (pratical classes);
- the quizzes mark (AD) is obtained by the average of the best 4 marks achieved by each student.

TG - Teamwork assignment:
Small teamwork assigment based on an online competition.

EF - Final Exam
- written exam.

To pass this course, apart from a final grade no less than 10, is required a minimum grade of 7 in the final exam.

### Special assessment (TE, DA, ...)

Special evaluations will be made by a written exam.

### Classification improvement

Students may choose between:

- improving simultaneously components Quizzes (FA) and Final Exam (EF);

- improving only component Final Exam (FE).

Component teamwork assignments (TG) is not possible to improve.