1. General information
Course:
STATISTICS
Code:
56307
Type:
BASIC
ECTS credits:
6
Degree:
418 - UNDERGRAD. IN INDUSTRIAL ELECTRONICS AND AUTOMAT. ENGINEERING
Academic year:
2023-24
Center:
303 - E.DE INGENIERÍA INDUSTRIAL Y AEROESPOACIAL DE TOLEDO
Group(s):
41
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer:
ISIDRO HIDALGO ARELLANO
- Group(s):
41
Building/Office
Department
Phone number
Email
Office hours
MATEMÁTICAS
Isidro.Hidalgo@uclm.es
Lecturer:
LICESIO JESUS RODRIGUEZ ARAGON
- Group(s):
41
Building/Office
Department
Phone number
Email
Office hours
Edificio Sabatini / 1.47
MATEMÁTICAS
6489
l.rodriguezaragon@uclm.es
Available in Virtual Campus.
Request an appointment by email.
2. Pre-Requisites
In order for students to achieve the learning objectives described, they must have knowledge and skills that are supposed to be guaranteed in their training prior to accessing the University:
- Knowledge of basic geometry and trigonometry, basic mathematical operations (powers, logarithms, fractions), polynomials, matrices, differentiation, integration and graphic representation of functions.
- Basic skills in handling instruments: elemental handling of computers.
Although there are no formal incompatibilities, students who access a subject without having acquired the skills of the previous subjects, following the subject will be much more costly and difficult both in terms of time and effort.
3. Justification in the curriculum, relation to other subjects and to the profession
The Industrial Engineer is the professional who uses the knowledge of the physical, mathematical and statistical sciences, together with the engineering techniques, to develop his professional activity in aspects such as the control, instrumentation and automation of processes and equipment, as well as the design, construction, operation and maintenance of industrial products. This training allows you to successfully participate in the different branches that make up industrial engineering, such as mechanics, electricity, electronics, etc., adapt to changes in technology in these areas and, where appropriate, generate them, responding thus to the needs that arise in the productive and service branches to achieve the well-being of the society to which it is due.
4. Degree competences achieved in this course
| Course competences | |
|---|---|
| Code | Description |
| 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 |
| CEB01 | Ability to solve mathematical problems that may arise in engineering. Ability to apply knowledge of linear algebra; geometry, differential geometry, differential and partial differential equations, numerical methods, numerical algorithms, statistics and optimisation. |
| CG03 | Knowledge of basic and technological subjects to facilitate learning of new methods and theories, and provide versatility to adapt to new situations. |
| CG04 | Ability to solve problems with initiative, decision-making, creativity, critical reasoning and to communicate and transmit knowledge, skills and abilities in the field of industrial engineering. |
| CT02 | Knowledge and application of information and communication technology. |
| CT03 | Ability to communicate correctly in both spoken and written form. |
5. Objectives or Learning Outcomes
| Course learning outcomes | |
|---|---|
| Description | |
| Knowledge and interpretation of the fundamental measures of descriptive statistics, approximate two-dimensional data by regression analysis, the fundamentals of probability, estimating parameters of statistical models, constructing confidence intervals, testing hypotheses and making decisions. | |
| Ability to express oneself correctly orally and in writing and, in particular ability to use the language of mathematics as a way of accurately expressing the quantities and operations that appear in industrial engineering. Acquired habits of working in a team and behaving respectfully. | |
| Knowledge of the main approaches for solving by numerical methods, user level implementation of software packages for statistics, data processing, mathematical calculation and visualisation, planning algorithms and programming using a high-level programming language, visualising functions, geometric figures and data, designing experiments, analysing data and interpreting results. | |
| Additional outcomes | |
| Not established. |
6. Units / Contents
-
Unit 1: Descriptive Statistics: Fundamentals, Correlation and Regression.
-
Unit 2: Probability
-
Unit 3: Statistical Inference: Point and interval estimation, Parametric and Non-Parametric Hypothesis Tests.
ADDITIONAL COMMENTS, REMARKS
Lab practices
1.- Introduction to R statistical software and RStudio environment.
2.- Descriptive Statistics: Distribution graphs and descriptions with numbers.
3.- Normal distribution and correlation.
4.- Linear regression.
5.- Linear regression and contingency tables.
6.- Intervals and significance tests for the mean.
7.- Intervals and significance tests for means, standard deviations, proportions and ANOVA.
7. Activities, Units/Modules and Methodology
| Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
| Class Attendance (theory) [ON-SITE] | Lectures | CB02 CB03 CB04 CB05 CEB01 CG03 | 1.2 | 30 | Y | N | Prior to face-to-face classes, the sections of the Teaching Text that will be covered will be indicated. The teacher will explain in class those aspects of the theoretical development of the subject that he deems necessary so that the student can later work autonomously. In some sessions, students will be presented with theoretical cases that they must answer and that will be evaluated. | |
| Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | CB02 CB03 CB04 CEB01 CG03 CG04 CT02 CT03 | 0.6 | 15 | Y | N | In these sessions in the classroom, the teacher will solve some problems in which the theoretical aspects previously exposed are applied. After solving some type problems, the teacher will be available to the students to solve the problems of the Teaching Text. In some sessions, students will be presented with problems that they must answer and that will be evaluated. At the end of each topic, a collection of self-assessment exercises will be proposed, the purpose of which is for the student to be able to self-assess the knowledge acquired and resolve any doubts that may have arisen in the tutorials, which is why they are also a good feedback procedure. Once resolved, they must be delivered to the teacher digitized, through the moodle platform, with the required format and on the predetermined date. | |
| Class Attendance (practical) [ON-SITE] | Practical or hands-on activities | CB02 CB03 CB04 CG04 CT02 CT03 | 0.4 | 10 | Y | N | The proposed practices will be carried out using a free statistical package: R and the RStudio interface. The use of students' personal computers is recommended. At the end of each practice session, students will be presented with exercises that they must solve and that will be evaluated. | |
| Study and Exam Preparation [OFF-SITE] | Self-study | CB02 CB03 CB04 CB05 CEB01 CG03 CG04 CT02 CT03 | 3.6 | 90 | N | N | The student must prepare for each of the two progress tests. | |
| Formative Assessment [ON-SITE] | Assessment tests | CB02 CB03 CB04 CB05 CEB01 CG03 CG04 CT02 CT03 | 0.2 | 5 | Y | N | There will be a first evaluation test (scheduled at the beginning of the course through the coordinator) at the end of the second unit. This test has established a minimum grade of 4 out of 10 so that you can average in the final grade of the subject. A final evaluation test will be carried out to evaluate the subject globally. Students who have passed the first evaluation test will be examined only on topic three in this second evaluation test. The rest must take this global final exam. | |
| 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).
8. Evaluation criteria and Grading System
| Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
| Projects | 20.00% | 20.00% | The student will face individually, during lectures and problems, theoretical questions (cases). The evaluation will be carried out through the moodle/campusvirtual platform. This activity is non-recoverable. The student must solve the problems raised and submit the scanned self-assessment exercises (written by hand) on the indicated date, with the correct format and with a clean and clear presentation. This activity is non-recoverable. The evaluation will be carried out through the moodle/campusvirtual platform. Students who opt for non-continuous assessment must submit to a theoretical-practical questionnaire that assesses this knowledge. |
| Laboratory sessions | 10.00% | 10.00% | Each laboratory practice will have associated exercises to be solved through the use of the statistical package. The student must answer questions related to these exercises through the moodle/virtual campus platform during the development of the practice. This activity is non-recoverable. Students who opt for non-continuous assessment must take a practical questionnaire that assesses this knowledge. |
| Final test | 70.00% | 70.00% | The student must take two evaluation tests with a weight of 35% each. For students who have not reached the minimum grade of 4 in the first partial test, the final test will have a score of 70%. To average the final grade for the course, students must obtain a minimum grade of 4 in the partial tests. In the non-continuous evaluation, the final test will have a weight of 70%. |
| Total: | 100.00% | 100.00% |
According to art. 4 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 12.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).
Evaluation criteria for the final exam:
-
Continuous assessment:The evaluation criteria of the ordinary call are:
20% Work (T).
10% Laboratory Practices (PL).
35% First Partial Test (1PP).
Those who in 1PP obtain a grade lower than 4 out of 10: 70% Final Test (PF).
Those who in 1PP obtain a mark greater than or equal to 4 out of 10: 35% Second Partial Test (2PP).
If the grades achieved either in 1PP+2PP or in PF are less than or equal to 3.5 out of 10, the Final Grade can never be greater than 4.5 out of 10.
The subject is considered passed by averaging the assigned weights and obtaining a Final Grade greater than or equal to 5 out of 10. -
Non-continuous evaluation:The final test corresponding to the non-continuous evaluation will have a weight of 70% for these students, who must also take a second theoretical test (theoretical concepts) and practice (R programming language) that will have a weight of 30%.
These two evaluable activities will be mandatory and the student must obtain a minimum grade of 4 out of 10 in each test.
The subject is considered passes by averaging the assigned weights and obtaining a Final Grade greater than or equal to 5 out of 10.
Specifications for the resit/retake exam:
The evaluation criteria of the extraordinary call are:
20% Work (T).
10% Laboratory Practices (PL).
70% Final Test (PF).
If the grade achieved in PF is less than or equal to 3.5 out of 10, the Final Grade can never be greater than 4.5 out of 10.
The subject is considered passed by averaging the assigned weights and obtaining a Final Grade greater than or equal to 5 out of 10.
20% Work (T).
10% Laboratory Practices (PL).
70% Final Test (PF).
If the grade achieved in PF is less than or equal to 3.5 out of 10, the Final Grade can never be greater than 4.5 out of 10.
The subject is considered passed by averaging the assigned weights and obtaining a Final Grade greater than or equal to 5 out of 10.
Specifications for the second resit / retake exam:
In this case, the same procedure established for the non-continuous evaluation will be applied.
9. Assignments, course calendar and important dates
| Not related to the syllabus/contents | |
|---|---|
| Hours | hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 30 |
| Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 15 |
| Class Attendance (practical) [PRESENCIAL][Practical or hands-on activities] | 10 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 90 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 5 |
| Formative Assessment [PRESENCIAL][Assessment tests] | 5 |
| Global activity | |
|---|---|
| Activities | hours |
| General comments about the planning: | This time distribution is indicative and may be modified if the particular circumstances, arising during the development of the course, so advise. |
10. Bibliography and Sources
| Author(s) | Title | Book/Journal | Citv | Publishing house | ISBN | Year | Description | Link | Catálogo biblioteca |
|---|---|---|---|---|---|---|---|---|---|
| A. J. Arriaza Gómez y otros | Estadística Básica con R y R Commander | UCA | 978-84-9828-186-6 | Libro Libre | http://knuth.uca.es/ebrcmdr |
|
|||
| C. M. Cuadras | Problemas de probabilidades y estadística | PPU | 84-86130-06-9 | Signatura Biblioteca: 519.2 CUA |
|
||||
| C. Pérez López | Estadística : problemas resueltos y aplicaciones | Pearson educación | 84-205-3780-2 | 2003 | Signatura Biblioteca: 519.2 PER |
|
|||
| D. Peña | Fundamentos de estadística | Alianza Editorial | 978-84-206-8380-5 | 2008 | Signatura Biblioteca: 519.2 PEÑ | http://site.ebrary.com/lib/bibliotecauclm/detail.action?adv.x=1&docID=11028686&f00=all&p00=Estad%C3%ADstica |
|
||
| D. S. Moore | Estadística aplicada básica | Antoni Bosch | 978-84-95348-04-3 | 2009 | TEXTO DOCENTE Signatura Biblioteca: 519.2 MOO | http://site.ebrary.com/lib/bibliotecauclm/docDetail.action?docID=10609557 |
|
||
| E. Gutiérrez González y O. Vladimirovna Panteleeva | Estadística inferencial para ingeniería y ciencias | Grupo Editorial Patria | 9786077444879 | 2016 | http://site.ebrary.com/lib/bibliotecauclm/detail.action?adv.x=1&docID=11379359&f00=all&p00=Estad%C3%ADstica | ||||
| F.J. Martín Pliego López y otros | Problemas de inferencia estadística | Thomson- Paraninfo | 84-9732-355-6 | 2002 | Signatura Biblioteca: 519.2(076) MAR |
|
|||
| H. A. Quevedo Urías y B. R. Pérez Salvador | Estadística para ingeniería y ciencias | Grupo Editorial Patria | 9786074389395 | 2014 | http://site.ebrary.com/lib/bibliotecauclm/detail.action?docID=11013660 | ||||
| I. Espejo Miranda y otros | Estadística descriptiva y probabilidad: teoría y problemas | UCA | 978-84-9828-467-6 | 2009 | http://site.ebrary.com/lib/bibliotecauclm/detail.action?docID=10844601 | ||||
| I. Espejo Miranda, F. Fernández Palacín y M.A. López Sánchez | Inferencia estadística: teoría y problemas | Servicio de Publicaciones de la Universidad de Cádiz | 9788498285581 | 2016 | https://ebookcentral.proquest.com/lib/bibliotecauclm-ebooks/detail.action?docID=4626891 | ||||
| J.L. Devore | Probabilidad y estadística para ingeniería y ciencias.6ª edición. | Thomson | 970-686-457-1 | 2005 | Signatura Biblioteca: 519.2 DEV |
|
|||
| M. Febrero Bande y otros | Prácticas de Estadística en R | Universidad Santiago de Compostela | 978-84-691-0975-1 | 2008 | http://eio.usc.es/pub/pateiro/files/pubdocentepracticasestadistica.pdf | ||||
| M. H. DeGroot | Probabilidad y estadística | Addison-Wesley Iberoamericana | 0-201-64405-3 | 1988 | Signatura Biblioteca: D 10454 |
|
|||
| M. T. González Manteiga y A. Pérez de Vargas | Estadística aplicada una visión instrumental | Ediciones Díaz de Santos | 9788479789138 | 2009 | https://ebookcentral.proquest.com/lib/bibliotecauclm-ebooks/detail.action?docID=3189079 | ||||
| Montgomery, Douglas C. | Probabilidad y estadística aplicadas a la ingeniería | Limusa Wiley, | 978-968-18-5915-2 | 2007 |
|
||||
| R.S. Kenet y S. Zacks | Estadística Industrial Moderna | Thomson | 970-686-027-4 | 2000 | Signatura Biblioteca: 519.2 KEN |
|
|||
| S. J. Álvarez Contreras | Estadística aplicada | CLAG | 84-921847-4-4 | 2011 | Signatura Bibilioteca: 519.2 ALV |
|
|||
| S. M. Ross y T. Valdés Sánchez | Introducción a la estadística | Editorial Reverté | 9788429151916 | 2014 | https://ebookcentral.proquest.com/lib/bibliotecauclm-ebooks/detail.action?docID=5635443 | ||||
| W. Mendenhall | Probabilidad y estadística para ingeniería y ciencias | Pretice Hall | 968-880-960-8 | 1997 | Signatura Biblioteca: D 519.2(076) MEN |
|
|||
| Walpole, Ronald E. | Probabilidad y estadística para ingeniería y ciencias | Pearson Educación | 978-970-26-0936-0 | 2007 |
|