Base Knowledge
Knowledge Mathematics at the first year of BSc. level and Probability and Statistics at a non higher education level.
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 and manipulate and analyze data.
Learning Results
Provide the fundamentals of Statistics and Probability necessary for the study and understanding of phenomena of interest in your area of training. Learn the main concepts and methods of interpretation and data processing. To know the probabilistic models that constitute the basis of statistical inference. Learn how to use and interpret basic tools of statistical inference. Use of Excel.
Program
1. Descriptive statistics.
Organization of data in frequency tables and graphical representation. Location measures and dispersion measures. Contingency tables. Location and scatter measures for two-dimensional data. Covariance and correlation coefficient.
2. Introduction to probability theory.
Probability of an event (definition and properties). Conditional probability and composite probability. Independence of events. Central Limit Theorem: fundamentals/simulation. Random variables. Distribution function. Discrete and continuous random variables: one and two dimensional. Random variables’ parameters: expect value, moments, median and quantiles. Variance and standard deviation. Expect value and variance proprieties. Discrete distributions: binomial, hypergeometric and Poisson. Continuous distributions: uniform, normal and exponential.
3. Statistical Inference.
Brief introduction to sample design. Random sample. Sampling distributions. Point estimation and interval estimation. Confidence interval for mean values, proportions and variances in normal populations. Introduction to hypothesis tests for mean, standard deviation and proportion. Applications.
4. Problem solving with Excel or R.
Curricular Unit Teachers
Grading Methods
- - Exame - 100.0%
Internship(s)
NAO
Bibliography
Recommended (available for free online)
Professor’s notes, available in Moodle.
Several authors (2020), ALEA – Ação Local Estatística Aplicada, Instituto Nacional de Estatística, http://www.alea.pt
Complementary
Pedrosa, A. e Gama, S. (2018) – Introdução Computacional à Probabilidade e Estatística com Excel, Porto Editora
Reis, E., Melo, P., Andrade, R. e Calapez, T. – Estatística Aplicada – Vols. 1 e 2, Edições Sílabo
Reis, E., Melo, P., Andrade, R. e Calapez, T. – Exercícios de Estatística Aplicada – Vols. 1 e 2, Edições Sílabo
Ross, Sheldon (2014) – Introduction to Probability and Statistics for Engineers and Scientists, Elsevier
Ryan, T. (2007) – Modern Engineering Statistics, Wiley
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