Base Knowledge
Recommended Knowledge
Fundamental notions of set algebra, differential and integral calculus.
Teaching Methodologies
The teaching method is initially expository and inquisitive, with the introduction of concepts and techniques, accompanied by examples with application in the field of engineering, and then proceeds with the resolution of exercises by students, under the guidance of the teacher. The resolution of exercises using statistical functions of graphing calculators will be promoted. Whenever justified, computer tools (Excel) will also be used.
Learning Results
Objectives: to provide the fundamentals of Statistics necessary for the analysis and interpretation of data, especially in the area of health sciences; present the main probability models with application in engineering areas.
Generic Skills: application of knowledge and understanding; critical spirit and interpretation of results; self-learning; ability to work in groups, developing interpersonal relationships.
Specific Skills: learn the main methods of data interpretation and analysis and the probabilistic models that form the basis of statistical inference; learn the basic tools of statistical inference, confidence intervals and hypothesis testing; to master the technique of simple linear regression, using the method of least squares in the estimation of parameters and the determination of confidence intervals and tests in the regression; use statistical software.
Program
Syllabus
1. Introduction
1.1. Descriptive statistics and inferential statistics
1.2. Population and sample
2. Probability
2.1. Fundamental concepts
2.2. Random experiment, space of outcomes and events
2.3. Notion of Probability
2.4. Conditional probability and independence
3. Random variables and main theoretical distributions
3.1. Discrete Random Variables and Continuous Random Variables
3.2. Location and dispersion parameters
3.3. Discrete special distributions:
3.3.1. Hypergeometric
3.3.2. Binomial
3.3.3. Poison
3.4. Continuous special distributions:
3.4.1. Uniform
3.4.2. Exponential
3.4.3. Normal
4. Sampling
4.1. Sampling distributions
5. Introduction to statistical inference
5.1. Point estimation and Interval estimation
5.2. Confidence intervals and hypothesis tests for population parameters
6. Linear regression
6.1. Scatter diagram, correlation and linear regression
6.2. Simple linear regression model
6.3. Confidence intervals and tests in regression
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Recommended Bibliography:
Canova, F. & Marques, M. . Apontamentos e exercícios de apoio às aulas. ISEC (available on academic platform InforEstudante)
Guimarães, R., Cabral, J. (2007). Estatística, McGraw-Hill, Lisboa (available at ISEC library: 3-3-239)
Pedrosa, A. & Gama, S. (2018). Introdução Computacional à Probabilidade e Estatística com Excel (3ª ed.). Porto: Porto Editora. (available at ISEC library: 3-3-236)
Complementary Bibliography:
Montgomery, D., Runger, G. (2007). Applied Statistics and Probability for Engineers. 4th ed. Wiley, New York. (available at ISEC library: 3-3-192)
Murteira, B., Ribeiro, C., Silva, J., Pimenta, C. (2002). Introdução à Estatística. McGraw Hill. (available at ISEC library: 3-3-148)
Ross, S. (2009). Introduction to probability and statistics for engineers and scientists. 4th ed. Amsterdam. Elsevier. (available at ISEC library: 3-3-191)