Identifying Data 2019/20
Subject (*) Statistics Code 614G01008
Study programme
Grao en Enxeñaría Informática
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
First Basic training 6
Language
Spanish
English
Teaching method Face-to-face
Prerequisites
Department Matemáticas
Coordinador
Lorenzo Freire, Silvia
E-mail
silvia.lorenzo@udc.es
Lecturers
Aneiros Perez, German
Cao Abad, Ricardo
Carpente Rodriguez, Maria Luisa
Costa Bouzas, Julian
Francisco Fernandez, Mario
García Jurado, Ignacio
González Rueda, Ángel Manuel
Lombardía Cortiña, María José
Lorenzo Freire, Silvia
Meilán Vila, Andrea
Noceda Dávila, Diego
Presedo Quindimil, Manuel Antonio
Vilar Fernandez, Juan Manuel
E-mail
german.aneiros@udc.es
ricardo.cao@udc.es
luisa.carpente@udc.es
julian.costa@udc.es
mario.francisco@udc.es
ignacio.garcia.jurado@udc.es
angel.manuel.rueda@udc.es
maria.jose.lombardia@udc.es
silvia.lorenzo@udc.es
andrea.meilan@udc.es
diego.noceda@udc.es
manuel.antonio.presedo.quindimil@udc.es
juan.vilar@udc.es
Web
General description Estatística descritiva. Análise exploratoria de datos. Probabilidade. Modelos de probabilidade. Inferencia estatística.

Study programme competencies
Code Study programme competences
A1 Capacidade para a resolución dos problemas matemáticos que se poden presentar na enxeñaría. Aptitude para aplicar os coñecementos sobre: álxebra linear; cálculo diferencial e integral; métodos numéricos; algorítmica numérica; estatística e optimización.
B3 Capacidade de análise e síntese
C2 Dominar a expresión e a comprensión de forma oral e escrita dun idioma estranxeiro.

Learning aims
Learning outcomes Study programme competences
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
B3
C2
Knowing how to analyze data using descriptive techniques and how to perform inference of population features from partial information, collected by random sampling, using statistical techniques. A1
B3
C2
Knowing how to model in simple random contexts using probabilistic tools A1
B3
C2

Contents
Topic Sub-topic
Probability Definition of probability. Properties
Conditional probability. Bayes’ theorem
Random variables Discrete random variables
Continuous random variables
Central limit theorem
Simulation
Descriptive statistics Frequency distributions
Graphical representations
Location and dispersion measures
Statistical inference Introduction
Point estimation
Confidence intervals
Parametric hypothesis tests
Nonparametric hypothesis tests
Simple regression Simple linear regression
Nonlinear regression

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A1 B3 C2 30 48 78
Laboratory practice A1 B3 C2 20 20 40
Seminar A1 B3 C2 10 10 20
Mixed objective/subjective test A1 B3 C2 3 3 6
 
Personalized attention 6 0 6
 
(*)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
Guest lecture / keynote speech
Laboratory practice
Seminar
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.

Assessment
Methodologies Competencies Description Qualification
Laboratory practice A1 B3 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, who have not been evaluated of laboratory practices, may perform a specific test to retrieve the 20% of the mark corresponding to that part.
20
Seminar A1 B3 C2 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 A1 B3 C2 The final exam, with a value between 60% and 80% (depending on Chapters 1 and 2 written control grades), will consist of a theoretical and a practical written test. 60
 
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, in writing, the final exam 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 (e.g. pen or calculator).

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, who 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.


Sources of information
Basic 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

Complementary Gonick, L. y Smith, W. (2001). Á estatística ¡en caricaturas!. SGAPEIO
Quintela del Río, A. (2013). El estadístico accidental. El autor
R Development Core Team (2000). Introducción a R. http://www.r-project.org/
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. (2005). Probabilidad y Estadística para Ingeniería y Ciencias. Thomson
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


Recommendations
Subjects that it is recommended to have taken before
Calculus/614G01003

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus
Statistical Methods/614G01057

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.