Guías Docentes Electrónicas
1. General information
Course:
STATISTICS
Code:
56307
Type:
BASIC
ECTS credits:
6
Degree:
353 - UNDERGRADUATE DEGREE PROG. IN MECHANICAL ENGINEERING (CR)
Academic year:
2022-23
Center:
602 - E.T.S. INDUSTRIAL ENGINEERING OF C. REAL
Group(s):
20  21 
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: VICTOR MANUEL CASERO ALONSO - Group(s): 20  21 
Building/Office
Department
Phone number
Email
Office hours
Politécnico/2-A15
MATEMÁTICAS
926052867
victormanuel.casero@uclm.es

Lecturer: RAUL RIVILLA BASTANTE - Group(s): 20  21 
Building/Office
Department
Phone number
Email
Office hours
3.27
MATEMÁTICAS
raul.rivilla@uclm.es

2. Pre-Requisites

In order to students achieve the described learning objectives, they must possess knowledge and skills that are supposed acquired from their pre-university education:

  • Knowledge: basic mathematical operations (powers, logarithms, fractions), polynomials, matrices, derivation, integration and graphic representation of functions.
  • Basic skills in managing computers.
3. Justification in the curriculum, relation to other subjects and to the profession

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.


4. Degree competences achieved in this course
Course competences
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
5. Objectives or Learning Outcomes
Course learning outcomes
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.
6. Units / Contents
  • Unit 1: Descriptive Statistics.
    • Unit 1.1: Frequency distributions.
    • Unit 1.2: Graphical representation.
    • Unit 1.3: Statistical measures.
    • Unit 1.4: Bidimensional distributions. Regression and correlation.
  • Unit 2: Probability Calculus.
    • Unit 2.1: Probability concepts and properties.
    • Unit 2.2: Random variables.
    • Unit 2.3: Moments of random variables.
    • Unit 2.4: Remarkable distributions of random variables.
  • Unit 3: Statistical Inference.
    • Unit 3.1: Statistical Inference. Point estimation.
    • Unit 3.2: Confidence Interval estimation.
    • Unit 3.3: Parametric Hypothesis Test.
    • Unit 3.4: Nonparametric methods.
    • Unit 3.5: Analysis of Variance.
    • Unit 3.6: Design of experiments.
ADDITIONAL COMMENTS, REMARKS

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.


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 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).

8. Evaluation criteria and Grading System
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%  
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:
    Correct approach of the problems.
    Correct results.
    Correct written expression.
    Minimum grade to pass the subject: 5 points out of 10.
  • Non-continuous evaluation:
    Correct approach of the problems.
    Correct results.
    Correct written expression.
    Minimum grade to pass the subject: 5 points out of 10.

Specifications for the resit/retake exam:
Same as final exam.
Specifications for the second resit / retake exam:
Same as final exam.
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Individual tutoring sessions [PRESENCIAL][] 1
Final test [PRESENCIAL][Assessment tests] 3

Unit 1 (de 3): Descriptive Statistics.
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.
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.
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
Activities hours
10. Bibliography and Sources
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 Ficha de la biblioteca
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 Ficha de la biblioteca
CUADRAS, Carles M. Problemas de probabilidades y estadística EUB 84-89607-09-5 (o.c.) 1995 Libro de Problemas Ficha de la biblioteca
Devore, Jay L. Probabilidad y estadística para ingeniería y ciencias Thomson 970-686-457-1 2005 Libro de Teoría Ficha de la biblioteca
Fernández Guerrero, Mercedes Manual de estadística para ingenieros Casa Ruiz Morote 84-934398-2-8 2007 Ficha de la biblioteca
García Pérez, Alfonso Ejercicios de estadística aplicada Universidad Nacional de Educación a Distancia 978-84-362-5547-8 2008 Libro de Problemas Ficha de la biblioteca
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 Ficha de la biblioteca
Novo Sanjurjo, Vicente Problemas de cálculo de probabilidades y estadística Sanz y Torres 84-96094-14-6 2003 Libro de Problemas Ficha de la biblioteca
Peña, Daniel Regresión y diseño de experimentos Alianza Editorial 84-206-8695-6 2002 Libro de Teoría Ficha de la biblioteca
Peña, Daniel Fundamentos de estadística Alianza Editorial 84-206-8696-4 2001 Libro de Teoría Ficha de la biblioteca
Sarabia Viejo, Angel Problemas de probabilidad y estadística : elementos teóricos Clagsa 84-604-5619-6 1993 Libro de Problemas Ficha de la biblioteca
Verzani, John Using R for introductory statistics Chapman and Hall/CRC 1-58488-450-9 2005 Libro de Prácticas de Ordenador Ficha de la biblioteca
Walpole, Ronald E. Probabilidad y estadística para ingenieros Prentice-Hall Hispanoamericana 970-17-0264-6 1999 Libro de Teoría Ficha de la biblioteca



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