Curricular Unit:			Code:
Statistics			997EST
Year:	Level:	Course:	Credits:
1	Undergraduate	Quality, Environment and Safety Management	4 ects
Learning Period:		Language of Instruction:	Total Hours:
Spring Semester		Portuguese/English	52
Learning Outcomes of the Curricular Unit:
Randomness and uncertainty are characteristics common to many phenomena. In this context, the objectives of the course are the introduction and development of knowledge and techniques for collecting and analyzing data and inference necessary for the modeling of random variables. The expected learning outcomes for this UC are as follows: - describe, analyze and draw conclusions about uni and bi-varied data - calculate probabilities and apply the concept of probability as a measure of uncertainty; - identify and apply the main theoretical models of probability distribution; - estimate confidence intervals and test hypotheses about the parameters and behavior of a given population.
Syllabus:
1 Basic concepts 2 Descriptive Statistics 3 Probability Theory 4. Random variables and probability distributions 5 Confidence interval estimation 6 Hypothesis Tests
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
Points 1 and 2 of the syllabus provide concepts and techniques for data collection and analysis and for summarize data, which allow describing, analysing and establishing simple conclusions about uni and bi-varied data. Points 3 and 4 introduce fundamental concepts concerning probability theory and random variables allowing the calculation of probabilities in simple and compound events and the understanding and application of main theoretical probability distribution models. Points 5 and 6 introduce the essential methods of statistical inference to parameters estimation and hypotheses testing. The contents are in accordance with the objectives of the course, comprising concepts and techniques involved in data collection, data analysis and inference, necessary to model random variables.
Teaching Methodologies (Including Evaluation):
The methodology of teaching and learning is expository, interrogative and demonstrative. Drawing on problem solving and oriented study geared to allow understanding and application of fundamental concepts and methods applied in descriptive statistics and statistical inference. The assessment includes: • Three written tests evaluation (90%) • Student performance, including attendance, resolution of proposed problems and active participation in classes. (10%)
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
The proposed methodologies are consistent with the learning objectives of the unit, because they establish a close and continuous coordination between theoretical concepts, practical examples and problem solving. This approach enables the interpretation and practical application of statistical concepts and methods.
Reading:
[1] Dekkin, F.M., Karaikamp, C., Lopuhaã, H.P., Meester, L. E. (2010). A Modern Introduction to Probability and Statistics. Understanding Why and How. Springer. [2] Guimarães, R.C. e Cabral, J. A. S. (2010) Estatística. Verlag Dashofer. [3] Montgomery, D.C., Runger, G. C. (2014) Applied Statistics and Probability for Engineers. Wiley. [4] Reis, E.; Melo, P; Andrade, R e Calapez, T. (2015) Estatística Aplicada. Vol. 1 e 2. Edições Sílabo. [5] Lapponi, J.C. Estatística usando Excel, 4ª Edição, Campus - Elsevier, 2005. ISBN 85-352-1574-3
Lecturer (* Responsible):
Alzira Dinis (madinis@ufp.edu.pt)