Statistics is a vital tool for engineering, since it allows to understand phenomena subject to variations as well as to predict and control them efficiently. In one way or another, statistical techniques are used in all branches of science. In our society, progress can be measured by various numerical indices. Statistics is used to describe, manipulate and interpret these numbers.



The subject “Statistical Methods of Engineering” consists of a syllabus that covers both theoretical and practical aspects related to Statistics and and practical aspects related to Statistics and Probability.



In order to achieve an optimal follow up of the subject, it is recommended that the student has knowledge of the following courses Algebra, Calculus and Computer Science.

BASIC COMPETENCIES:

CB5 -That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy. further studies with a high degree of autonomy.



GENERAL COMPETENCES:

C3 - Knowledge in basic and technological subjects, which enables them to learn new methods and theories, and gives them the versatility to adapt to new situations. theories, and endow them with versatility to adapt to new situations.



TRANSVERSAL COMPETENCES:

C12 - To adopt a responsible attitude, orderly at work and willing to learn considering the challenge that the challenge posed by the necessary continuous training.

C13 - Apply the strategies typical of scientific methodology: analyze the problematic situation qualitatively and quantitatively, propose hypotheses and solutions using the models typical of industrial engineering.



SPECIFIC COMPETENCES:

FB1 - Ability to solve mathematical problems that may arise in engineering. Ability 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.

1 lesson: Descriptive Statistics.

2 lesson: Combinatorics and Probability.

3 lesson: Discrete random variables.

4 lesson: Continuous random variables.

5 lesson: Regresion analysis.

Master classes, classroom practices and computer lessons will be the followed methodology.



Master classes and classroom practices timetable ans classrooms will be fixed and published by the School.



Computer classes timetable and classrooms will be published for each subgroup.



If it is not possible to carry out the classroom teaching, the teaching will be done online.



The student will have to work on what has been developed in class, studying the theory, doing problems, preparing the practices, etc. in the non classroom hours.

• Continuous Assessment System
• Final Assessment System
• Tools and qualification percentages:
  • Written test to be taken (%): 70
  • Realization of Practical Work (exercises, cases or problems) (%): 15
  • Individual works (%): 15

The evaluation will be carried out according to the article number 8 of the "Normativa reguladora de la evaluación del alumnado en las titulaciones oficiales de grado". The evaluation will be continous with tasks and activities carried out along the term. This evaluation will be completed with a final exam that will be held in the exams period.



The activities or tests to be performed during the training period will be the following:



Computer practices:

The practices carried out throughout the term will be evaluated by means of a test to be performed using the computer tools acquired by the student.



Occasional evaluation of the learning process:

Suggested exercises will be collected by the professor.The realization of the exercise will be individual and the exercise not delivered at the time will have a grade of 0. The grade will be the average of all exercises



Writing exam:

It will be held on the date, time and place published by the direction of the School. Its development will be, preferably, face-to-face and written. If it cannot be carried out in these terms, the exam will be held online, prior notification to the students. The exam will consist of theoretical and practical questions that will evaluate the competences to be acquired in the course.



Final grade:

- Grade of the computer practices: 15 %

- Grade of the ocasional evaluation of the learning process: 15 %

- Grade or writing exam: 70 %



According to the article 8.3 of the regulations, students who want to be evaluated through the final evaluation system, regardeless of wether or not have participated in the continuous evaluation system, must submit to the responsible lecturer a resignation letter. For that, the students will have a period of 9 weeks from the beginning of the term. In this case, the final evaluation will consist of a writing exam (85 % of the final grade) and a computer practice exam (15 % of the final grade).



In any case, any student who does not take the writing exam will receive a grado of "NOT PRESENTED".



In genera, and unless otherwise indicated, during the development of an evaluation test, the use of books, notes, as well as phones, electronic devices, computer, or other devices is forbidden for students. The use of a calculator is permitted. At the time of the exam, if necessary, the places where unauthorized materials may be deposited can be indicated, so that they are out of reach of students.

Final exam:

It will be held on the date, time and place published by the School management and will consist of a writing exam and a computer practice exam, with theoretical and practical questions that will evaluate the competences to be acquired in the course.



Final grade:

- Grade of the computer practices exam: 15 %

- Grade or writing exam: 85 %

The lecturer will give to the students the necessary material.

Basic bibliography

Agirre, E. "Estatistikaren oinarriak. Ariketak". Ed. Udako Euskal Unibertsitatea

Canavos, G. "Probabilidad y Estadística. Aplicaciones y métodos". Ed. McGraw-Hill

Devore, J.L. "Probabilidad y Estadística para ingeniería y ciencias". Ed. Thomson.

Fernández, K. "Estatistikarako Sarrera". Udako Euskal Unibertsitatea

Fernández, K. Orbe, J., Zubia, M. "Estatistikarako Sarrera. Ariketak". Udako Euskal Unibertsitatea

Fernández, K. Orbe, J., Zubia, M. "Estatistika I eta Estatistika II. Ariketak". Udako Euskal Unibertsitatea

Fernández, V., Zelaia, A. "Optimizazioa. Programazio Lineala". Udako Euskal Unibertsitatea

González Manteiga, M.T., Pérez de Vargas Luque, A. "Estadística Aplicada: Una visión instrumental". Ed. Díaz de Santos, S.A.

González Manteiga, M.T. "400 problemas resueltos de Estadística multidisciplinar". Ed. Díaz de Santos, S.A.

Quirk, T. "Excel 2019 for Engineering Statistics". Ed. Springer

Uña, I., Tomeo, V., San Martín, J. "Lecciones de Cálculo de Probabilidades". Ed. Thomson.

In-depth bibliography

Etxeberria, J. "Estatistika. Teoria eta praktika SPSSWIN erabiliz". Ed. Elhuyar.
Dokuchaev, N. "Probability Theory". Ed. World Scientific Pub. Co. Inc.
Gnedenko, B. "Teoría de las probabilidades". Ed. Rubiños.
Lipschutz, S., J. Schiller, J. "Introducción a la probabilidad y estadistica". Ed. McGraw-Hill.
Parra, I. "Problemas de Inferencia Estadistica". Ed. Thomson.

Web addresses

Descartes: http://descartes.cnice.mec.es/indice_ud.php
Eustat - Euskal Estatistika Erakundea - Instituto Vasco de Estadística: www.eustat.eus
Instituto Nacional de Estadística: www.ine.es
Ingeniaritzako metodo estatistikoak. Praktikak MAXIMA kalkulu sinbolikorako software askea erabiliz
https://ocw.ehu.eus/course/view.php?id=60
Properties of one-dimensional random variables: theory and practice https://ocw.ehu.eus/course/view.php?id=527
Propiedades de las variables aleatorias unidimensionales: Teoría y práctica: https://ocw.ehu.eus/course/view.php?id=578
Stat Trek: https://stattrek.com/
Zorizko aldagai unidimentsionalen ezaugarriak: Teoria eta praktika
https://ocw.ehu.eus/course/view.php?id=507

Links revised as of 05/30/2024