Teaching Methodologies
Theoretical classes primarily utilize an expository and inquisitive method, introducing concepts and techniques accompanied by examples associated with real-world data applicable to the field of engineering, whenever possible. In practical classes, students must solve the proposed exercises under the guidance of the professor. Computer tools (e.g., Excel/R) will be used whenever justified. Student support is provided through clarification of theoretical doubts and problem-solving during classes and office hours.
Learning Results
Knowledge: The fundamental concepts of Probability and Statistics necessary for the study, analysis, and interpretation of data, and for the study and use of models with applications in the areas of industrial engineering, are presented.
Generic Skills: Application of knowledge and understanding. Critical thinking and interpretation of results. Communication. Self-learning. Ability to work in a group, developing interpersonal relationships.
Specific Skills: Learning the main concepts of Probability and Statistics, for a follow-up and understanding of the topics to be covered in this curricular unit, namely, learning and knowing how to use the methods of data interpretation and analysis and the probabilistic models that constitute the basis of statistical inference. Learning to use and interpret basic tools of statistical inference.
Program
1. Introduction to Probability Theory
Definitions of Probability. Properties. Conditional probability and independence. Total probability theorem and Bayes’ theorem.
2. Random Variables
Definition. Density function and distribution function. Discrete and continuous random variables. Parameters: mean, moments, median, and quantiles. Variance and standard deviation. Properties of the mean and variance. Discrete distributions: binomial, hypergeometric, and Poisson. Continuous distributions: uniform, exponential, and normal. Reference to the t-Student, Chi-square, and F-Snedecor distributions.
3. Introduction to Statistical Inference
Notions of sampling. Random sample. Sampling distributions. Point and interval estimation: confidence intervals for the expected value, for the proportion, for the variance of a normal population, for the difference of expected values, for the difference of proportions, and for the quotient of variances of normal populations. Hypothesis testing
Curricular Unit Teachers
Luis Manuel dos Santos de Melo MargalhoGrading Methods
- - Exam - 100.0%
Internship(s)
NAO
Bibliography
– Apontamentos e folhas de exercícios das aulas teórico-práticas (disponível nas plataformas Moodle e InforEstudante)
– Hogg, R., Tanis, E. (1997) Probability and statistical inference, 5th ed., Prentice Hall (disponível na biblioteca do ISEC: 3-3-109)
– Guimarães, R., Cabral, J. (2007) Estatística, McGraw-Hill, Lisboa (disponível na biblioteca do ISEC: 3-3-239)
– Murteira, B., Ribeiro, C., Silva, J., Pimenta, C. (2002) Introdução à Estatística. McGraw Hill. (disponível na biblioteca do ISEC: 3-3-148)
– Montgomery, D., Runger, G. (2007) Applied Statistics and Probability for Engineers. 4th ed. Wiley, New York. (disponível na biblioteca do ISEC: 3-3-192)
– Ross, S. (2009) Introduction to probability and statistics for engineers and scientists. 4th ed. Amsterdam. Elsevier (disponível na biblioteca do ISEC: 3-3-191)