Study programme competencies |
Code
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Study programme competences / results
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A1 |
CE1 - Capacidade para utilizar con destreza conceptos e métodos propios da matemática discreta, a álxebra lineal, o cálculo diferencial e integral, e a estatística e probabilidade, na resolución dos problemas propios da ciencia e enxeñaría de datos. |
A2 |
CE2 - Capacidade para resolver problemas matemáticos, planificando a súa resolución en función das ferramentas dispoñibles e das restricións de tempo e recursos. |
A3 |
CE3 - Capacidade para a análise de datos e a comprensión, modelado e resolución de problemas en contextos de aleatoriedade. |
B1 |
CB1 - Que os estudantes demostrasen posuír e comprender coñecementos nunha área de estudo que parte da base da educación secundaria xeral, e adóitase atopar a un nivel que, aínda que se apoia en libros de texto avanzados, inclúe tamén algúns aspectos que implican coñecementos procedentes da vangarda do seu campo de estudo |
B5 |
CB5 - Que os estudantes desenvolvesen aquelas habilidades de aprendizaxe necesarias para emprender estudos posteriores cun alto grao de autonomía |
B6 |
CG1 - Ser capaz de buscar e seleccionar a información útil necesaria para resolver problemas complexos, manexando con soltura as fontes bibliográficas do campo. |
C1 |
CT1 - Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida. |
C2 |
CT2 - Estimular a capacidade para traballar en equipos interdisciplinares ou transdisciplinares, para ofrecer propostas que contribúan a un desenvolvemento sustentable ambiental, económico, político e social. |
Learning aims |
Learning outcomes |
Study programme competences / results |
Have knowlegde about statistical techniques and knowing how to use them for the exploratory data analysis. |
A1 A2 A3
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B1 B5 B6
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C1
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Have knowlegde and understand the general concepts about probability models. |
A1 A2 A3
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B1 B5 B6
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C1 C2
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Knowing how to model in simple random contexts using probabilistic tools. |
A1 A2 A3
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B1 B5 B6
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C1
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Knowing how to use auxiliary computer tools for Statistics: statistical packages and programming languages with statistical orientation; and knowing how to critically interpret the results. |
A1 A2 A3
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B1 B5 B6
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C1
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Contents |
Topic |
Sub-topic |
Probability |
Definition of probability. Properties
Conditional probability. Bayes’ theorem
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Univariate random variables |
Discrete random variables
Continuous random variables
Central limit theorem
Applications: Reliability and simulation
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Multivariate random variables |
Bivariate discrete random variables
Bivariate continuous random variables
Marginal distributions
Conditionated distributions
Independent random variables
Characteristic measures
Multivariate random variables |
Descriptive statistics |
Frequency distributions
Graphical representations
Location and dispersion measures
Two dimensional statistical variable
Linear simple regression |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A1 A3 B5 |
30 |
48 |
78 |
Laboratory practice |
C1 C2 |
20 |
16 |
36 |
Seminar |
A2 B6 |
10 |
10 |
20 |
Mixed objective/subjective test |
B1 |
4 |
0 |
4 |
|
Personalized attention |
|
12 |
0 |
12 |
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(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Students will receive lectures where the professor, with the help of relevant audiovisual media, will present the theoretical and practical contents of the subject. Participation and debate will be encouraged at all times. |
Laboratory practice |
Laboratory practices will be held in a computer lab. It will be learned how to use the free statistical software R, and its programming structures. Statistical studies using both real and simulated data will be performed. |
Seminar |
Seminars will reinforce both the applied nature of the subject and its interactivity. Students will be able to express their doubts and concerns regarding the subject, and they will have the opportunity to perform, with the professor supervision, similar questions to those proposed in the exams. Additionally, with a very individualized attention, they will be able to complete the lab practices. |
Mixed objective/subjective test |
Students will have to show proficiency in the theoretical aspects of the subject and their ability to solve problems in the field of probability and statistics. |
Personalized attention |
Methodologies
|
Seminar |
Guest lecture / keynote speech |
Laboratory practice |
|
Description |
For problem solving, it will be important to personally help students with the questions that may arise. This attention will also serve, on the one hand, to the professor to detect potential problems in the methodology used to teach the subject and, on the other hand, to the students to strengthen theoretical knowledge and to express their concerns about the subject. |
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Assessment |
Methodologies
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Competencies / Results |
Description
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Qualification
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Seminar |
A2 B6 |
During the course, students will prove their interest in the subject and his mastery of it by performing two written tests (controls), each with a maximum mark of 10%. These two tests will correspond to Chapters 1 and 2 of the course.
Students who do not obtain the maximum of 20% of the mark corresponding to this part will be able to retrieve the remaining part when taking the final exam of the subject. |
20 |
Mixed objective/subjective test |
B1 |
The final exam, with a value between 60% and 80% (depending on Chapters 1 and 2 written control grades), will consist of a mixed theoretical/practical test. |
60 |
Laboratory practice |
C1 C2 |
Students will develop lab practice exercises specifically designed to assess their monitoring of the subject. The correct completion of these exercises will be supervised by the professor in the classroom. To evaluate the degree of understanding and learning of these practices, 2 or 3 assessment tests will be scheduled. They will be performed during the laboratory classes having a 20% of the final grade.
For enrolled full-time students, the practice mark is not retrievable by performing another test. Enrolled part-time students, if they have not been evaluated of laboratory practices, may perform a specific test to retrieve the 20% of the mark corresponding to that part.
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20 |
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Assessment comments |
Students will finish the class period with a maximum of 40% of the grade, achieved with the two written tests (10% each) and the two or three tests evaluating the laboratory practices (20%). On the date set by the Faculty in its annual program, students will perform the final test of the subject (60%), where they will have to answer theoretical questions, solve theoretical and practical issues, and calculate the solution of several problems. For this test, students will only bring the material expressly authorized. The grade obtained in the final exam (60%) will be re-scaled so that students will have the opportunity to retrieve the 20% of the mark corresponding to the written controls (the 20% of the laboratory practice assessment mark cannot be retrieved). Thus, depending on the score obtained by the student in the two written controls, the highest score of the final exam will be between 6 and 8 points (out of 10). Thus, denoting by P the laboratory practice grade (between 0 and 2 points), denoting by C the written controls (Chapters 1 and 2) final grade (between 0 and 2 points) and denoting by F the final exam grade (between 0 and 10 points), the course final grade will be P+C+0'1*(8-C)*F. The day of the final exam, part-time students, if they have not been previously evaluated for the laboratory practice part, will be able to perform a specific test to retrieve the 20% of the mark corresponding to that part. In the second-chance, the marks obtained by continuous evaluation (the two controls and the tests of the laboratory practices) are maintained and the student only has to repeat the final exam. This will be of the same type and with the same weight in the final mark that in the first-chance, that is, the same formula will be applied to calculate the final grade, but now F is the grade that the student has obtained in the second-chance final exam. The fraudulent performance of the tests or evaluation activities will directly imply the grade of failure (0) in the subject. The evaluation system in the case of academic dispensation will be the same as that described in this section.
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Sources of information |
Basic
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Eguzkitza Arrizabalaga, J.M. (2014). Laboratorio de estadística y probabilidad con R. Gami Editorial
Cao, R., Francisco, M., Naya, S., Presedo, M.A., Vázquez, M., Vilar, J.A. y Vilar, J.M. (2001). Introducción a la Estadística y sus aplicaciones. Ediciones Pirámide |
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Complementary
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Gonick, L. y Smith, W. (2001). Á estatística ¡en caricaturas!. SGAPEIO
Blasco Lorenzo, A. y Pérez Díaz, S. (2015). Modelos aleatorios en ingeniería. Paraninfo
Montgomery, D.C. y Runger, G.C. (2004). Probabilidad y Estadística aplicadas a la Ingeniería. McGraw-Hill
Devore, J.L. (2008). Probabilidad y Estadística para Ingeniería y Ciencias. Thomson
Walpole, R.E., Myers, S.L. y Myers, R. (2000). Probabilidad y Estadística para Ingenieríos. Prentice Hall
Hernández, V., Ramos, E. y Yáñez, I. (2007). Probabilidad y sus aplicaciones en Ingeniería Informática. Ediciones Académicas
Ugarte, M.D., Militino, A.F., Arnholt, A.T. (2008). Probability and Statistics with R. Chapman and Hall/CRC
Horgan, J.M. (2009). Probability with R. An Introduction with Computer Science Applications. Wiley |
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Recommendations |
Subjects that it is recommended to have taken before |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
Regression Models/614G02012 | Statistical Modeling of High Dimensional Data/614G02013 | Statistical Inference/614G02007 |
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