ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 Total: 150
Use languages Spanish, Galician, English
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Higher Technical Engineering School
Call: Second Semester
Teaching: Sin docencia (Extinguida)
Enrolment: No Matriculable | 1st year (Yes)
The main goal of this course is to make the students familiar with the basic concepts and techniques of Descriptive Statistics, Probability and Statistical Inference, especially applied to Engineering field.
CHAPTER 1. DESCRIPTIVE STATISTICS
1.1 Basic concepts.
1.2 Frequency distributions.
1.3 Graphical representations.
1.4 Location, scale and shape measurements.
1.5 Bidimensional descriptive statistics. Contingency tables and the regression line.
CHAPTER 2. BASICS OF PROBABILITY
2.1 Random experiment. Random events.
2.2 Probability. Operations over random events.
2.3 Conditional probability. Independent events.
2.4 Some probabilistic results
CHAPTER 3. DISCRETE RANDOM VARIABLES
3.1 Discrete random variable: probability mass and distribution function.
3.2 Characteristic measurements.
3.3 Discrete distribution models.
CHAPTER 4. CONTINUOUS RANDOM VARIABLES
4.1 Continuous random variable: density and distribution functions.
4.2 Characteristic measures
4.3 Continuous distribution models.
4.4 Distributions approximations.
CHAPTER 5. AN INTRODUCTION TO STATISTICAL INFERENCE AND PARAMETER ESTIMATION
5.1 An introduction to statistical inference.
5.2 Distributions in normal populations.
5.3 Parameter estimation for mean, variance and proportion. Properties.
5.4 Estimation by confidence intervals.
CHAPTER 6. HYPOTHESIS TESTING
6.1 Introduction to hypothesis testing.
6.2 Testing procedure.
6.3 Hypothesis testing in Gaussian populations.
COMPUTER LABS CONTENTS: introduction to R; descriptive statistics; sampling distributions; confidence intervals and hypothesis tests; simple linear regression model.
BASIC AND COMPLEMENTARY BIBLIOGRAPHY AVAILABLE ONLINE:
Espejo Miranda, I. (2014). Estadística descriptiva y Probabilidad. Universidad de Cádiz. Disponible en https://prelo.usc.es/.
Febrero Bande, M., Galeano San Miguel, P., González Díaz, J. and Pateiro López, B. (2008). Estadística: ingeniería técnica en informática de sistemas. Universidade de Santiago de Compostela. Disponible en https://docplayer.es/493297-Estadistica-ingenieria-tecnica-en-informati….
García García, V.J. (2014). Estadística descriptiva y Probabilidad. Universidad de Cádiz. Disponible en https://prelo.usc.es/.
Montero Fernández, J. and Minuesa Abril, C. (2018). Estadística básica para Ciencias de la Salud. Universidad de Extremadura. Disponible en http://matematicas.unex.es/~jmf/Archivos/Manual%20de%20Bioestad%C3%ADst….
Montero Alonso, M.A. Inferencia, Estimación y Contrastes de hipótesis. Universidad de Granada. Disponible en https://www.ugr.es/~eues/webgrupo/Docencia/MonteroAlonso/estadisticaII/….
Paradis, E. (2003). R para principiantes. R Cran. Disponible en https://cran.r-project.org/doc/contrib/rdebuts_es.pdf.
Recursos TIC del Ministerio de Educación, Cultura y Deporte (Proyecto Descartes). http://recursostic.educacion.es/descartes/web/materiales_didacticos/Mue… o http://recursostic.educacion.es/descartes/web/materiales_didacticos/inf….
Venables, W.N., Smith, D.M. and the R Core Team (2020). An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics (Version 3.6.3). Disponible en https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
Walpole, R.E., Myers, R.H., Myers, S.L. and Ye, K. (2012). Probabilidad y estadística para ingenierías y ciencias. Pearson. Disponible en https://vereniciafunez94hotmail.files.wordpress.com/2014/08/8va-probabi….
BASIC AND COMPLEMENTARY BIBLIOGRAPHY:
Cao, R. et al. (2005) Introducción a la Estadística y sus Aplicaciones. Pirámide.
Ross, S. (2014) Introduction to Probability and Statistics for Engineers and Scientists, Fifth Edition. Academic Press.
Cao, R. et al. (1998) Estadística básica aplicada. Tórculo Edicións.
Devore, J.L. (2005) Probabilidad y Estadística para Ingeniería y Ciencias. Paraninfo.
Febrero, M. et al. (2008) Estadística: ingeniería técnica en informática de sistemas. USC.
Mendenhall, W.M. and Sincich, T.L. (2016) Statistics for Engineering and the Sciences. CRC Press, Boca Ratón.
Montgomery, D. and Runger, G. (1996) Probabilidad y Estadística aplicadas a la Ingeniería. McGraw-Hill.
Peña, D. (1991) Estadística, Modelos y Métodos (1 y 2). Alianza Universal.
Quesada, V. and García, A. (1988) Lecciones de Cálculo de Probabilidades. Díaz de Santos.
In this course, our aim is to contribute to prepare the students in the competencies mentioned in the proposal for the Degree in Computer Sciences (USC): CG5, CG8, CG9, CG10, TR1,TR2, TR3, FB1, FB3, RI6 and TI5. The following module-specific competencies (form the Mathematics Module) will be also introduced:
- Present and formulate in a clear way the hypothesis and tools used for problem solving, using the proper terminology.
- Reinforce the habit of stating questions and solving problems as scientific practice.
- Know the mathematical notation, methods and vocabulary for modelling and case study.
- Know the mathematical tools that allow for solving problems in the engineering context.
- Use scientific software for solving mathematical problems in the engineering context.
- Apply the basic probabilistic and statistisc concepts, and use them in problem solving.
With respect to this last competency, more specifically, this course will also introduce the following competencies:
-Know how to synthetize and describe a large amount of data, choosing the right statistics depending on the variable character, and analyze the existing relations among them.
- Know how to formulate real problems in statistical terms.
- Know how to estimate unknown parameters from a given sample.
- Know how to compare two populations from characteristic unknown parameters.
- Understand the general principles in the more usual probabilistic models.
- Understand the principles and applications of hypothesis testing methods.
- Be able to plan, coordinate and organize a work project.
- Be able to work in a team, in a collaborative way, and also to appreciate the self-learning capacity.
- Gain facility with statistical packages.
- Be critical and responsible.
Activities for enhancing competences (general, basic, cross-area, computer science and information technology specific), as well as the assessment system will be detailed when describing the teaching methodology and the assessment system. Module-specific as well as course specific competences are enhanced in all the teaching activities designed for this course.
Stage 1 (adapted normality)
Lectures (24 hours): during the lectures, the professor will introduce the theoretical and practical concepts, making use of a multimedia presentation. Some typical problems and exercise will be also solved, so the student can work on the exercise assignments. Apart from the recommended bibliography, the student will have access to all the material elaborated for the course. General competences CG8, CG9, TRw (critical reasoning) and TR3 (self-working capacity) will be strengthened during lectures, as well as RI6 and FB3 (knowledge of mathematical basic concepts).
Interactive (24 hours): interactive sessions are distributed in seminars for exercise solving and computer labs. During the computer labs, the student will be introduced to the statistical package R. In these sessions, the students will also work on the practical assignments. In order to follow the practical labs, the students will have a guide for the lab session. During interactive sessions, the following competences will be fostered: CG5, TR1 (analytical thinking; organization and planning; communication), FB1 (mathematical problem solving in engineering) and TI5. Team-working activities will be carried out during interactive sessions to enhance competence TR2.
Small groups (3 hours): these sessions in small groups are intended to keep tracking of the students work. We will a series of activities to provide the student with a global view of the subject. At the same time, these activities will help the students to detect which topics or techniques need a further revision. During small groups sessions, TR1 and TR2 will be strenghthen, especially regarding communication capacity and critical thinking.
Stage 2 (distance)
In the stage 2, would keep face-to-face the interactive classes, seminars and computer labs. On the other hand, lectures will be online using the corporate platform MS Teams. The material of the subject will be available in the Campus Virtual and besides the/the students/ace will have of explanatory videos of the matter, exercises resolved or complementary material for the computer labs Likewise, small groups will perform through the email or of the platform MS Teams.
Stage 3 (closing of the installations)
In the stage 3, all the teaching would be no face-to-face. The means employed in this case would be the already exposed in the stage 2.
In any of the considered stages, distribution of lectures (24 hours) and seminars (10 hours) according to the different chapters is the following:
Chapter 1. Descriptive statistics: 3 lectures, 2 seminars.
Chapter 2. Basics of probability: 4 lectures, 1 seminar.
Chapter 3. Discrete R.V.: 5 lectures, 1 seminar.
Chapter 4. Continuous R.V.: 4 lectures, 2 seminars.
Chapter 5. Introduction to statistical inference and parameter estimation: 4 lectures, 2 seminars.
Chapter 6. Hypothesis testing: 4 lectures, 2 seminars.
Computer labs (14 hours) will be organized in 7 sessions. Labs contents will be linked to one or several chapters. As an example, a possible distribution of labs is the following:
Lab 1. An introduction to R. (2 hours)
Lab 2. Descriptive statistics. (2 hours, Chapter 1)
Lab 3. Random variables (2 hours, Chapter 3, Chapter 4)
Lab 4. Transformation of r.v. (2 hours, Chapter 3, Chapter 4)
Lab 5. Regression (2 hours, Chapter 2, Chapter 4)
Lab 6. Sampling distributions (2 hours, Chapter 5)
Lab 7. Confindece intervals and hypothesis testing in normal populations (2 hours, Chapter 5, chpater 6)
Stage 1 (adapted normality)
Continuous assessment (30%): continuous assessment will be based on the student participation in different activities. Continuous assessment activities will include case studies (individual or in working groups) using R. The professors will also propose some exercise assignments to be individually solved during lectures or at home. The continuous assessment grade will be preserved along the academic year. In order to concurr in the continuous assessment evaluation, students must attend at least 75% of the practical sessions. Students who had attended labs in one of the past two academic years excempt from attendance requirement.
Final exam (70%): the final exam will include some theoretical and practical questions on the subject contents. Outputs of statistical analysis with the package used in the computer lab may be included for their interpretation.
Stage 2 (distance)
In the stage 2, the assessment system will be the same that the described in the stage 1 and will treat to keep face-to-face the proofs in which they can fulfil the conditions required by the rule of the USC. If they could not be face-to-face the controls that form part of the continuous evaluation, as well as the final exam, would be no face-to-face. For the realisation of exams would use the Campus Virtual for the download of the exam and the delivery of the same once filled by the student in a time limited. It will perform the supervision of the exams by means of the corporate platform MS Teams: the students will have to be connected during the proof to the session that each group will have with the teachers. The supervision will not be recorded, but the teachers can request, if they estimate it timely, the exhibition of a document of identification.
Stage 3 (closing of the installations)
In the stage 3, the assessment system will be the same that the described in the stage 1 and all the proofs will be no face-to-face. These proofs will be such as they are described in the stage 2.
Notice that, for the cases of fraudulent realisation of exercises or exams, will be of application the collected in the “Normativa de evaluación del rendimiento académico de los estudiantes y de la revisión de las calificaciones”.
In any of the considered stages, with the different tasks that will be proposed during the course, the level of general competences (CG5, CG8, CG9 and CG10) and TI5 will be assessed. Some specific features such as team-working (TR2) and self-working capacity (TR3) will be also assessed. Moreover, thanks to the final exam, apart from the module and course specific competences, TR1 and TR2 (written communication capacity, critical thinking), FB3 (knowledge of mathematical basic concepts) and FB1 (mathematical problem solving in engineering), as well as general competences related to problem solving abilities (CG8, CG9 and CG10) will be assessed with the final exam.
Finally, a student will be considered as attending the evaluation when he/she has participated in activities that could reach, at least, 50% of the final evaluation. The weight of the continuous assessment se in the extraordinary opportunity will be the same as for the ordinary assessment period.
In this subject, the student must attend 48 hours of lectures (24 lectures and 24 interactive). For each lecture hour, about 1.5 hours is considered necessary for the revision of concepts and bibliography consulting.
For the interactive part, one hour of self-study for each session hour would be enough for revising the class work. Apart from this work, the students must be aware of the importance of being able to solve problems (from the exercise assigments or from the recommended bibliography).
Following the lectures and interactive sessions is crucial for following the course and passing the assessment. The student must work on all the activities recommended by the professor (solving exercise, revising bibliography and practical exercises) in order to pass the course.
Subject specific teaching material will be posted at the Virtual Campus (USC).
Contingency Plan
Teaching methodology:
Stage 2 (distance)
In the stage 2, would keep face-to-face the interactive classes, seminars and computer labs. On the other hand, lectures will be online using the corporate platform MS Teams. The material of the subject will be available in the Campus Virtual and besides the/the students/ace will have of explanatory videos of the matter, exercises resolved or complementary material for the computer labs Likewise, small groups will perform through the email or of the platform MS Teams.
Stage 3 (closing of the installations)
In the stage 3, all the teaching would be no face-to-face. The means employed in this case would be the already exposed in the stage 2.
Assessment system:
Stage 2 (distance)
In the stage 2, the assessment system will be the same that the described in the stage 1 and will treat to keep face-to-face the proofs in which they can fulfil the conditions required by the rule of the USC. If they could not be face-to-face the controls that form part of the continuous evaluation, as well as the final exam, would be no face-to-face. For the realisation of exams would use the Campus Virtual for the download of the exam and the delivery of the same once filled by the student in a time limited. It will perform the supervision of the exams by means of the corporate platform MS Teams: the students will have to be connected during the proof to the session that each group will have with the teachers. The supervision will not be recorded, but the teachers can request, if they estimate it timely, the exhibition of a document of identification.
Stage 3 (closing of the installations)
In the stage 3, the assessment system will be the same that the described in the stage 1 and all the proofs will be no face-to-face. These proofs will be such as they are described in the stage 2.
This guide and the criteria and methodologies in her described are subject to the modifications that derive of rules and guidelines of the USC.
Mercedes Conde Amboage
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mercedes.amboage [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Maria Isabel Borrajo Garcia
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaisabel.borrajo [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Jose Ameijeiras Alonso
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813165
- jose.ameijeiras [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
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| 10:00-11:00 | Grupo /CLIS_01 | Spanish, Galician | Classroom A1 |
| 11:00-12:00 | Grupo /CLIS_02 | Galician, Spanish | Classroom A1 |
| 12:00-13:00 | Grupo /CLIS_03 | Galician, Spanish | Classroom A1 |
| 17:30-19:30 | Grupo /CLIL_02 | Galician | Computer Room I7 |
| Tuesday | |||
| 11:00-12:00 | Grupo /CLE_01 | Galician, Spanish | Classroom A1 |
| 17:30-19:30 | Grupo /CLIL_04 | Galician | Computer Room I7 |
| Wednesday | |||
| 11:00-12:00 | Grupo /CLE_01 | Galician, Spanish | Classroom A1 |
| 17:30-19:30 | Grupo /CLIL_01 | Galician | Computer Room I7 |
| Thursday | |||
| 17:30-19:30 | Grupo /CLIL_03 | Galician | Computer Room I7 |
| 01.15.2021 09:15-14:00 | Grupo /CLIL_02 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLE_01 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLIS_03 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLIL_03 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLIS_01 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLIL_01 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLIL_04 | rest room / dining room |
| 01.15.2021 09:15-14:00 | Grupo /CLIS_02 | rest room / dining room |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_04 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLE_01 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIS_02 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_02 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIS_03 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_03 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIS_01 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_01 | Classroom A1 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_03 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLIS_01 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_01 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_04 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLE_01 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLIS_02 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLIL_02 | Classroom A2 |
| 05.19.2021 09:15-14:00 | Grupo /CLIS_03 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIL_02 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIS_03 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIL_03 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIS_01 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIL_01 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIL_04 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLE_01 | Classroom A2 |
| 07.12.2021 09:15-14:00 | Grupo /CLIS_02 | Classroom A2 |