1. General information
Course:
STATISTICAL TECHNIQUES FOR BUSINESS
Code:
54336
Type:
ELECTIVE
ECTS credits:
6
Degree:
320 - UNDERGRADUATE DEGREE IN BUSINESS MANAGEMENT AND ADMINISTRATION (CR)
Academic year:
2020-21
Center:
403 - FACULTY OF LAW AND SOCIAL SCIENCES OF C. REAL
Group(s):
20
29
Year:
4
Duration:
First semester
Main language:
Spanish
Second language:
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer:
MIGUEL ANGEL TARANCON MORAN
- Group(s):
20
29
Building/Office
Department
Phone number
Email
Office hours
Facultad Derecho y CCSS de Ciudad Real /1.12 Módulo E
ECONOMÍA APLICADA I
ext. 3537
miguelangel.tarancon@uclm.es
2. Pre-Requisites
It is recommended, although not compulsory, to have previously passed the subjects of Business Statistics and Statistical Inference and Introduction to Econometrics.
3. Justification in the curriculum, relation to other subjects and to the profession
Nowadays, it is very frequent in the business world the availability of large volumes of data and the handling of computer tools that allow the adequate extraction of the information they contain. In this process, the knowledge and use of appropriate statistical techniques is fundamental to the discovery of new and significant relationships and patterns of behavior within the data. The objective of the course is to provide the student with the necessary tools for the representation, description and extraction of patterns and relationships between variables in multidimensional data, which is known in the statistical literature as "data mining".
4. Degree competences achieved in this course
| Course competences | |
|---|---|
| Code | Description |
| E07 | Understand the economic environment as a result and application of theoretical or formal representations on how the economy works. To do so, it will be necessary to be able to understand and use common handbooks, as well as articles and, in general, leading edge bibliography in the core subjects of the curriculum. |
| E08 | Ability to produce financial information, relevant to the decision-making process. |
| G01 | Possession of the skills needed for continuous, self-led, independent learning, which will allow students to develop the learning abilities needed to undertake further study with a high degree of independence. |
| G03 | Develop oral and written communication skills in order to prepare reports, research projects and business projects and defend them before any commission or group of professionals (specialised or non-specialised) in more than one language, by collecting relevant evidence and interpreting it appropriately so as to reach conclusions. |
| G04 | Ability to use and develop information and communication technologies and to apply them to the corresponding business department by using specific programmes for these business areas. |
5. Objectives or Learning Outcomes
| Course learning outcomes | |
|---|---|
| Description | |
| Search for information in order to analyze it, interpret is meaning, synthesize it and communicate it to others. | |
| Know the tools and methods for the quantitative analysis of the company and its environment, including models for business decision making as well as economic forecast models. | |
| Know the analytical models and techniques of the economic and legal environment currently faced by enterprises, with special attention given to the search for opportunities and the anticipation of potential changes. | |
| Work out problems in creative and innovative ways. | |
| Additional outcomes | |
| Not established. |
6. Units / Contents
-
Unit 1: Introduction to Multivariate Analysis. The Multivariate Normal Distribution.
-
Unit 2: Analysis of Variance
-
Unit 3: Dimension Reduction Techniques.
-
Unit 4: Clustering Techniques
-
Unit 5: Techniques for Qualitative Data Analysis
-
Unit 6: Other Techniques for Data Analysis in Business
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 | E08 G01 G03 G04 | 0.9 | 22.5 | N | N | Face-to-face lectures, in which the teacher will focus on the subject and explain its fundamental contents. | |
| Class Attendance (practical) [ON-SITE] | Combination of methods | E08 G01 G03 G04 | 0.9 | 22.5 | N | N | Classroom practice: exercises, seminars, debates. | |
| Writing of reports or projects [OFF-SITE] | Group Work | E08 G01 G03 G04 | 2 | 50 | Y | Y | Group workshops. At the beginning of the course, working groups will be created and a project will be assigned to them. The project will be developed throughout the course. | |
| Final test [ON-SITE] | Assessment tests | G01 G04 | 0.08 | 2 | Y | Y | Preparation and performance of written test with questionnaire and exercises to be solved. | |
| Study and Exam Preparation [OFF-SITE] | Self-study | E08 G01 G04 | 1.6 | 40 | N | N | Student's autonomous work tutored by the teacher. | |
| Total: | 5.48 | 137 | ||||||
| Total credits of in-class work: 1.88 | Total class time hours: 47 | |||||||
| 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 |
| Assessment of problem solving and/or case studies | 20.00% | 0.00% | The student will have to solve and submit a selection of problems provided by the teacher throughout the course. |
| Final test | 40.00% | 100.00% | Written test of a theoretical-practical nature. |
| Assessment of active participation | 10.00% | 0.00% | The student's active participation in the classroom will be valued. |
| Fieldwork assessment | 30.00% | 0.00% | At the beginning of the course, working groups will be created and a project will be assigned to them, which will be developed throughout the course. These projects will be led by the teachers and can be displayed at the end of the course. |
| 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 final exam may be replaced by increasing the weight of the fieldwork and problem solving and/or case studies part.
-
Non-continuous evaluation:The performance of the different training activities to be evaluated will be facilitated to students who cannot benefit from the system of continuous evaluation.
Specifications for the resit/retake exam:
In the resit/retake exam the student will be evaluated of all the competences associated to the different formative activities of the subject through the taking of a final test whose structure and composition will be communicated in advance by the lecturer.
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
| Not related to the syllabus/contents | |
|---|---|
| Hours | hours |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 20 |
| Final test [PRESENCIAL][Assessment tests] | 2 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 10 |
| Unit 1 (de 6): Introduction to Multivariate Analysis. The Multivariate Normal Distribution. | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 3 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 4 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 4 |
| Unit 2 (de 6): Analysis of Variance | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 3 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 4 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 4 |
| Unit 3 (de 6): Dimension Reduction Techniques. | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4.5 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 4.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 6 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 6 |
| Unit 4 (de 6): Clustering Techniques | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 3 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 4 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 4 |
| Unit 5 (de 6): Techniques for Qualitative Data Analysis | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4.5 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 4.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 6 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 6 |
| Unit 6 (de 6): Other Techniques for Data Analysis in Business | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4.5 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 4.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 6 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 6 |
| Global activity | |
|---|---|
| Activities | hours |
10. Bibliography and Sources
| Author(s) | Title | Book/Journal | Citv | Publishing house | ISBN | Year | Description | Link | Catálogo biblioteca |
|---|---|---|---|---|---|---|---|---|---|
| Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba | Quality control with R : an ISO standards approach | Springer | 978-3-319-24044-2 | 2015 | Para el tema de Otras técnicas | http://qualitycontrolwithr.com | |||
| Francois Husson, Sebastien Le, Jérôme Pagès | Exploratory Multivariate Analysis by Example Using R | Chapman and Hall/CRC | 9781138196346 | 2017 | Este libro cubre algunos de los métodos vistos en la asignatura con el paquete FactoMineR | ||||
| Garrett Grolemund, Hadley Wickham | R for Data Science | O'Reilly Media, Inc. | 9781491910382 | 2016 | Este libro cubre los aspectos relacionados con el software estadístico y lenguaje de programación R, orientado al análisis de datos | ||||
| Hair, J.F., Anderson, R.E., Tatham, R.L. y Black, W.C. | Análisis multivariante | Prentice Hall | 978-84-8322-035-1 | 2005 | Existen ediciones anteriores en la biblioteca de la Facultad |
|
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| Joaquín Aldás, Ezequiel Uriel | Análisis multivariante aplicado con R | Madrid | Alfa Centauro Paraninfo | 978-84-283-2969-9 | 2017 | Este es el manual principal de la asignatura. Cubre las técnicas de la asignatura y otras que no se ven pero pueden ser útiles, por ejemplo, para elaborar TFG. | |||
| Lévy, J.P. y Varela, J. (dirs) | Análisis multivariable para las ciencias sociales | Pearson Education | 978-84-205-3727-6 | 2008 | Existen ediciones anteriores en la biblioteca de la Facultad |
|
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| Pérez, César | Técnicas de análisis multivariante de datos : aplicaciones con SPSS | Pearson Educación | 978-84-205-4104-4 | 2008 |
|