Probabilities and Statistics A
Objectives
The aim of the course is to provide students a basic knowledge of Probabilities and Statistics which are an indispensable tool for decision making under uncertainty. In addition, the course helps students gain an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study.
At the end of the unit students will have acquired skills that enable them:
-Know and understand the basic elements of the theory and the calculus of probabilities
-Describe the main probabilistic distributions of discrete and continuous variables and applies them in the description of random phenomena
-Infer about population parameters based on sample distributions
-Build-statistical models, which establish a functional relationship between variables
General characterization
Code
12791
Credits
6.0
Responsible teacher
Vanda Marisa da Rosa Milheiro Lourenço
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
Basics of mathematical analysis, pointing out some topological notions, primitives, integrals and functions of more than one variable.
Bibliography
Guimarães, R.C. & Cabral, J.A.S. (2007), Estatística, McGraw-Hill.
Montgomery, D.C. & Runger, G.C. (2011), Applied Statistics and Probability for Engineers, John Wiley.
Pedrosa, A.C.& Gama, S.M.A. (2004), Introdução Computacional à Probabilidade e Estatística, Porto Editora.
Pestana, D.D. & Velosa, S.F. (2002) Introdução à Probabilidade e à Estatística, Fundação Calouste Gulbenkian, Lisboa.
Robalo, A. (1994), Estatística - Exercícios, vol. I, II, Edições Sílabo, Portugal.
Teaching method
Available soon
Evaluation method
CLASS ATTENDANCE - PARTICIPATION
This academic year, attendance in PE A classes is not mandatory. Therefore, all students have direct access to the evaluation assessments. However, the teaching staff recommends that students attend classes to enhance their success in the course.
CONTINUOUS ASSESSMENT
Continuous assessment will be based on two individual written tests.
The 1st test (T1) will take place in November (date to be determined), with a weight of 50%. The test will have a duration of 1.5 hours.
The 2nd test (T2) will take place in December (date to be determined), with a weight of 50%. The test will have a duration of 1.5 hours.
A student passes the course if they complete both tests and if the weighted average of the two tests is equal to or greater than 9.5 points.
Final grade = 50% T1 + 50% T2
ASSESSMENT DURING THE RESIT PERIOD
The assessment during the resit period is conducted through an individual written exam, which is valid both for grade improvement and for passing the course.
The exam is graded on a scale of 0 to 20 points. A student passes the course if he or she achieves a grade equal to or greater than 9.5 points on the exam.
A student who obtains a final grade equal to or greater than 17 may be required to take an oral defense of their grade (on a date to be arranged). If the student does not attend the oral exam, his or her final grade will be recorded as 17 points.
GRADE IMPROVEMENT
Students who wish to take the resit exam to improve their grade must request this improvement in advance from the academic services.
OTHER INFORMATION
Students should confirm that the email registered in CLIP is correct. Otherwise, they may not receive important notifications.
Due to the high number of enrolled students, the teaching staff may not be able to respond to all emails received. Emails should only be sent for important and urgent matters.
The use of scientific or graphing calculators is permitted during tests/exams, as long as they do not have Wi-Fi capability.
If fraud or plagiarism is proven in any of the assessment elements of the course, the students directly involved will be immediately failed in the course, without prejudice to potential disciplinary or civil action, and the incident will be reported to the Director of FCT.
Subject matter
1. Basic notions of probability.
2. Random variables and their probability distributions.
3. Moments of random variables.
4. Some important distributions.
5. Random vectors.
6. Central limit theorem.
7. Basic notions of statistics.
8. Point and interval estimation.
9. Hypothesis testing
10. Non-parametric tests
11. Simple linear regression
12. Experimental Error