This curricular unit aims at supplying to the students the theoretical and practical knowledge about methodologies on parametric and nonparametric statistical inference. Students will explore the core principles of statistics, from both the conceptual and applied perspectives. The students will acquire competences related to random variables, estimators, sampling distributions, point and interval estimation and hypothesis testing. The students will clearly understand the conditions of applicability of each procedure. The concepts and principles will be illustrated using real-world concepts applicable to many industries, including medical, business, sports, insurance, etc.
Ana Cristina Marinho da Costa
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
Afonso, A., Nunes, C. (2011). Estatística e Probabilidades. Aplicações e Soluções em SPSS, Escolar Editora.
Conover, W. J. (1999). Practical Nonparametric Statistics. 3rd ed., Wiley.
Hogg, R. V., Tanis, E. A. (2010). Probability and Statistical Inference. 8th Edition, New Jersey: Pearson/Prentice-Hall.
Newbold, P., Carlson, W. L., Thorne, B. (2012). Statistics for Business and Economics. 8th Edition, Boston: Pearson.
Murteira, B., Ribeiro, C.S., Silva, J.A. e Pimenta, C. (2002). Introdução à Estatística, McGraw Hill.
The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications, questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.
1st call: project (55%), 1st test (35%), 2nd test with minimum grade of 7,5 points (40%)
2nd call: final exam (100%)
The curricular unit is organized in eight Learning Units (LU):
LU1. Random variables
LU2. Probability distributions
LU3. Sampling distributions
LU4. Point estimation
LU5. Interval estimation
LU6. Hypothesis testing
LU7. Analysis of variance (ANOVA)
LU8. Nonparametric tests