Statistical Methods

Code:

EIC0105

Acronym:

MEST

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2019/2020 - 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 language

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.

Working method

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). Applications.


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

Software

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 - Quizzes:
   AD = 2/3 FA + 1/3 TG

AD - Quizzes:
- 6 quizzes (pratical classes);
- the quizzes mark (AD) is obtained by adding the average of the best 2 marks from the first 3 quizzes and the average of the best 2 marks from the last 3 quizzes.

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.