Métodos Estatísticos

Base Knowledge

Knowledge of high-school Mathematics.

Teaching Methodologies

In the theoretical classes will be used the expository method with discussion. The practical classes will be dedicated to problem solving under the guidance of the teacher. Some of the issues to address will allow students to use Excel or R or Python and manipulate and analyze data.

Learning Results

The goals of this course are:
a) Deepen the knowledge of Descriptive Statistics developed during Secondary Education;
b) Learn techniques of Inferential Statistics and its assumptions in order to use them wisely and critically,
acknowledge their limitations, and interpret correctly the results;
c) Use software to describe data and to apply the techniques of statistical decision and correctly interpret the
results.

Program

1. Descriptive Statistics
– Data organization
– Frequency tables and graphical representation
– Measures of location and dispersion
– Contingency tables and scatter plots

2. Statistical Inference
– Probability
– Random variable
– Discrete distributions: binomial, hypergeometric, and Poisson
– Continuous distributions: uniform, normal, and exponential
– Confidence intervals and hypothesis testing

3. Introduction to Regression

4. Introduction to Multivariate Statistics: dimensionality reduction and classification

Curricular Unit Teachers

Luis Manuel dos Santos de Melo Margalho

Grading Methods

The student can choose between continuous evaluation or evaluation by final exam.

Continuous evaluation - consists of a writen test and an assessment of regular class atendance. The student can
get approval if the sum of the previous components is equal to or greater than 9.5 values.

     The writen test will be graded with 12 points and will happen in the last week of classes.

     The evaluation of regular class atendance will be materialized through problems proposed during the
semester for a total of 8 points. The dates of realization of these problems will not be announced in
advance to students.

Final Exam - consists of conducting an exam quoted for 20 points, which will evaluate all chapters of the
program contents. Students who have not passed the continuous assessment or who have not chosen continuous assessment and those who are allowed to improve their rating may access the exam.


    Internship(s)

    NAO

    Bibliography

    Recommended (available for free online)

    Professor’s notes, available in Moodle and Inforestudante.

    Pedrosa, A. e Gama, S. (2018) – Introdução Computacional à Probabilidade e Estatística com Excel (3º ed.). Porto Editora (ISEC’s library: 3-3-236)

    Complementary

    Reis, E., Melo, P., Andrade, R. e Calapez, T. (2001) – Estatística Aplicada – Vols. 1 e 2. (4ª ed.). Edições Sílabo (ISEC’s library: 3-3-135)

    Ross, Sheldon (2014) – Introduction to Probability and Statistics for Engineers and Scientists, Elsevier (ISEC’s library: 3-3-191)

    Ryan, T. (2007) – Modern Engineering Statistics, Wiley (ISEC’s library: 3-3-159)

    R Core Team (2022)- An Introduction to R – Notes on R: A Programming Environment for Data Analysis and
    Graphics, https://cran.r-project.org/doc/manuals/R-intro.pdf, Version 4.2.1, 23/06/2022