In order to students achieve the described learning objectives, they must possess knowledge and skills that are supposed acquired from their pre-university education:
This course provides students with the necessary skills to face and solve the problems that a graduate can find in their work, mainly related to the analysis and treatment of data obtained empirically.
In addition, the concepts developed in this subject will be used later in compulsory subjects such as Electrical, Electronic and Automatic Technology, Manufacturing and Industrial Control Systems, and Manufacturing Technology. Some of these concepts also appear in several elective subjects.
For the Engineer, Statistics will be an essential work tool in his/her daily work. The basic responsibility of an Engineer is to lead the continuous improvement of quality and productivity in all processes that depend on him/her. But to improve processes it is necessary to change them, and these changes, if they are to be rational, can only be the result of data analysis. How to generate data that has relevant information? How to extract, by means of the adequate analysis, said information of the data? The answer to both questions is the object of Statistical Science and as a consequence every Engineer must know it and apply it in his daily work.
Course competences | |
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Code | Description |
A01 | To understand and have knowledge in an area of study that moves on from the general education attained at secondary level and usually found at a level that, while supported in advanced text books, also includes some aspects that include knowledge found at the cutting edge of the field of study. |
A02 | To know how to apply knowledge to work or vocation in a professional manner and possess the competences that are usually demonstrated by the formulation and defence of arguments and the resolution of problems in the field of study. |
A03 | To have the capability to gather and interpret relevant data (normally within the area of study) to make judgements that include a reflection on themes of a social, scientific or ethical nature. |
A07 | Knowledge of Information Technology and Communication (ITC). |
A08 | Appropriate level of oral and written communication. |
A12 | Knowledge of basic materials and technologies that assist the learning of new methods and theories and enable versatility to adapt to new situations. |
A13 | Ability to take the initiative to solve problems, take decisions, creativity, critical reasoning and ability to communicate and transmit knowledge, skills and abilities in Mechanical Engineering. |
A17 | Ability to apply principles and methods of quality control. |
B01 | Ability to solve mathematical problems that occur in engineering. Aptitude to apply knowledge of: linear algebra; geometry; differential geometry; differential and integral calculus; differential and partial differential equations; numerical methods; numerical algorithms; statistics and optimization. |
CB01 | Prove that they have acquired and understood knowledge in a subject area that derives from general secondary education and is appropriate to a level based on advanced course books, and includes updated and cutting-edge aspects of their field of knowledge. |
CB02 | Apply their knowledge to their job or vocation in a professional manner and show that they have the competences to construct and justify arguments and solve problems within their subject area. |
CB03 | Be able to gather and process relevant information (usually within their subject area) to give opinions, including reflections on relevant social, scientific or ethical issues. |
CB04 | Transmit information, ideas, problems and solutions for both specialist and non-specialist audiences. |
CB05 | Have developed the necessary learning abilities to carry on studying autonomously |
Course learning outcomes | |
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Description | |
Be able to express yourself correctly both orally and in writing, and, in particular, to know how to use mathematical language to express with precision quantities and operations that appear in industrial engineering. Become accustomed to working in a team and behaving respectfully. | |
Know and interpret the fundamental measurements of descriptive statistics, approximate bidimensional data through regression adjustment, know the fundamentals of probability, estimate the parameters of statistical models, construct confidence intervals, contrast hypotheses and take decisions. | |
Know the main approaches for resolution through using numerical methods, to use some statistical software packages at user level, data processing, mathematical calculus and vizualization, set out algorithms and program through programming language of a high level, vizualize functions, geometric figures and data, design experiments, analyze data and interpret results | |
Additional outcomes | |
Not established. |
Computer labs:
Lab 1: Introduction to the statistical software R and Descriptive Statistics.
Lab 2: Bivariate data, Multivariate and Linear Regression.
Lab 3: Probability distributions and Central Limit Theorem.
Lab 4: Confidence Intervals and Hypothesis tests (parametrics).
Lab 5: Parametric and nonparametric Hypothesis test.
Lab 6: Analysis of Variance.
Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | A01 A12 CB01 | 1.04 | 26 | N | N | Presentation of contents to the students. | |
Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | A02 A03 A08 A12 A13 B01 CB02 CB03 CB04 CB05 | 0.64 | 16 | N | N | Problem solving from a list of available exercises. | |
Computer room practice [ON-SITE] | Practical or hands-on activities | A01 A02 A03 A07 A08 A12 A13 A17 B01 CB01 CB02 CB03 CB04 CB05 | 0.48 | 12 | Y | N | Using R statistical software for problem solving. | |
Individual tutoring sessions [ON-SITE] | A01 A02 A03 A07 A08 A12 A13 A17 B01 CB01 CB02 CB03 CB04 CB05 | 0.04 | 1 | N | N | For solving doubts, ask for problem solving. | ||
Progress test [ON-SITE] | Assessment tests | A02 A03 A08 A12 A13 A17 B01 CB02 CB03 CB04 CB05 | 0.08 | 2 | Y | N | 1 or 2 progress test similar to the final exam. | |
Final test [ON-SITE] | Assessment tests | A02 A03 A08 A12 A13 A17 B01 CB02 CB03 CB04 CB05 | 0.12 | 3 | Y | Y | Final exam consists of 5 exercises: 1 related with theme 1, 1 related with theme 2, 2 related with theme 3 and a final exercise with theoretical and practical test questions and related with the R software. | |
Study and Exam Preparation [OFF-SITE] | Self-study | A01 A02 A03 A07 A08 A12 A13 A17 B01 CB01 CB02 CB03 CB04 CB05 | 3.6 | 90 | N | N | ||
Total: | 6 | 150 | ||||||
Total credits of in-class work: 2.4 | Total class time hours: 60 | |||||||
Total credits of out of class work: 3.6 | Total hours of out of class work: 90 |
As: Assessable training activity Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).
Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
Other methods of assessment | 10.00% | 10.00% | Individual or team work supervised. (Computer labs and progress test) |
Final test | 90.00% | 90.00% | Mean of the 5 final exam exercises/questions. |
Total: | 100.00% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
Individual tutoring sessions [PRESENCIAL][] | 1 |
Final test [PRESENCIAL][Assessment tests] | 3 |
Unit 1 (de 3): Descriptive Statistics. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 6 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 4 |
Computer room practice [PRESENCIAL][Practical or hands-on activities] | 4 |
Progress test [PRESENCIAL][Assessment tests] | .5 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 20 |
Unit 2 (de 3): Probability Calculus. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 6 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 4 |
Computer room practice [PRESENCIAL][Practical or hands-on activities] | 2 |
Progress test [PRESENCIAL][Assessment tests] | .5 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 20 |
Unit 3 (de 3): Statistical Inference. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 14 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 8 |
Computer room practice [PRESENCIAL][Practical or hands-on activities] | 6 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 50 |
Global activity | |
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Activities | hours |
Author(s) | Title | Book/Journal | Citv | Publishing house | ISBN | Year | Description | Link | Catálogo biblioteca |
---|---|---|---|---|---|---|---|---|---|
Ardanuy Albajar, Ramón | Estadística para ingenieros | Hespérides | 84-604-7675-8 | 1998 | Libro de Teoría | ||||
Arriaza Gómez, A.J. et al. | Estadística básica con R y R-Commander | Servicio de Publicaciones de la Universidad de | 978-84-9828-186-6 | 2008 | Libro de Prácticas de Ordenador | http://knuth.uca.es/ebrcmdr | |||
CUADRAS, Carles M. | Problemas de probabilidades y estadística | EUB | 84-89607-09-5 (o.c.) | 1995 | Libro de Problemas | ||||
Devore, Jay L. | Probabilidad y estadística para ingeniería y ciencias | Thomson | 970-686-457-1 | 2005 | Libro de Teoría | ||||
Fernández Guerrero, Mercedes | Manual de estadística para ingenieros | Casa Ruiz Morote | 84-934398-2-8 | 2007 | |||||
García Pérez, Alfonso | Ejercicios de estadística aplicada | Universidad Nacional de Educación a Distancia | 978-84-362-5547-8 | 2008 | Libro de Problemas | ||||
Letón, Emilio et al. | Mini-Vídeos de autoformación | http://minivideos.uc3m.es/ | |||||||
Montgomery, Douglas C. | Probabilidad y estadística aplicadas a la ingeniería | McGraw-Hill | 970-10-1017-5 | 1996 | Libro de Teoría | ||||
Novo Sanjurjo, Vicente | Problemas de cálculo de probabilidades y estadística | Sanz y Torres | 84-96094-14-6 | 2003 | Libro de Problemas | ||||
Peña, Daniel | Regresión y diseño de experimentos | Alianza Editorial | 84-206-8695-6 | 2002 | Libro de Teoría | ||||
Peña, Daniel | Fundamentos de estadística | Alianza Editorial | 84-206-8696-4 | 2001 | Libro de Teoría | ||||
Sarabia Viejo, Angel | Problemas de probabilidad y estadística : elementos teóricos | Clagsa | 84-604-5619-6 | 1993 | Libro de Problemas | ||||
Verzani, John | Using R for introductory statistics | Chapman and Hall/CRC | 1-58488-450-9 | 2005 | Libro de Prácticas de Ordenador | ||||
Walpole, Ronald E. | Probabilidad y estadística para ingenieros | Prentice-Hall Hispanoamericana | 970-17-0264-6 | 1999 | Libro de Teoría |