1. General information
Course:
STATISTICAL TECHNIQUES FOR BUSINESS
Code:
54336
Type:
ELECTIVE
ECTS credits:
6
Degree:
317 - UNDERGRADUATE DEGREE IN BUSINESS MANAGEMENT AND ADMINISTRATION
Academic year:
2019-20
Center:
5 - FACULTY OF ECONOMICS AND BUSINESS
Group(s):
12
Year:
4
Duration:
First semester
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer:
NOELIA GARCIA RUBIO
- Group(s):
12
Building/Office
Department
Phone number
Email
Office hours
Facultad de Ciencias Económicas y Empresariales. Despacho 3.13
ECONOMÍA APLICADA I
926053545
noelia.garcia@uclm.es
Ver la página web de la facultad y Moodle de la asignatura
2. Pre-Requisites
It is recommended to have coursed the subjects on Statistics for Business and Statistical Inference.
3. Justification in the curriculum, relation to other subjects and to the profession
Today it is very common, in the world of Economics and Business, to have a great amount of data and manage computer tools for proper extraction of the statistical information they contain.
In this process, the knowledge and use of appropriate statistical techniques is fundamental to the discovery of new and meaningful relationships and behavior patterns within the data. The aim of the course is to provide students with the tools necessary 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 | |
| Description | |
| The student will obtain the ability to conduct a preliminary analysis of the data, identifying relevant information and preparing it for further analysis. The student will know identify the appropriate statistical technique, based on the data available and taking into account their nature, to achieve the objectives. The student will get the ability to properly apply each statistical technique through appropriate tools, mainly using the statistical programming environment R. The student will be able to draw the relevant conclusions and know how to analyze and transmit them appropriately for decision making in a business economic scope. |
|
6. Units / Contents
-
Unit 1: Introduction to Multivariate Analysis
-
Unit 2: Analysis of variance
-
Unit 3: Data Reduction Methods
-
Unit 4: Clasification and Comparison of Groups
-
Unit 5: Models for Qualitative Data Analysis
-
Unit 6: Other Techniques for Business Data Analysis
7. Activities, Units/Modules and Methodology
| Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | R | Description * |
| Class Attendance (theory) [ON-SITE] | Lectures | E07 E08 G01 G03 G04 | 0.9 | 22.5 | N | N | N | |
| Class Attendance (practical) [ON-SITE] | Other Methodologies | E07 E08 G01 G03 G04 | 0.9 | 22.5 | N | N | N | |
| Study and Exam Preparation [OFF-SITE] | Self-study | E07 E08 G01 G04 | 1.6 | 40 | N | N | N | |
| Other on-site activities [ON-SITE] | Workshops and Seminars | E07 G01 G03 G04 | 0.52 | 13 | Y | N | N | |
| Writing of reports or projects [OFF-SITE] | Group Work | E07 E08 G01 G03 G04 | 2 | 50 | Y | Y | Y | |
| Final test [ON-SITE] | Assessment tests | E07 G01 G04 | 0.08 | 2 | Y | Y | Y | |
| 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 R: Rescheduling training activity
8. Evaluation criteria and Grading System
| Grading System | |||
| Evaluation System | Face-to-Face | Self-Study Student | Description |
| Assessment of active participation | 10.00% | 0.00% | The active attitude of the student will be classroom. |
| Fieldwork assessment | 30.00% | 0.00% | At the begining of the course working groups will be created and they will develop a project along the course. These projects will be supervised by the teacher and may need to be exposed at the end of the course. |
| Assessment of problem solving and/or case studies | 20.00% | 0.00% | The teacher will provide the student some tasks which will have to be solved and delivered at the end of each theme. |
| Final test | 40.00% | 0.00% | Written test with some practicals questions to be solved. |
| Total: | 100.00% | 0.00% | |
Evaluation criteria for the final exam:
The final test may be replaced by increasing the weight of the part corresponding to the resolution of problems or cases.
Specifications for the resit/retake exam:
You can only recover the qualifications of group work and problem solving (handing it over again according to teacher recommendations) and final test (exam). Qualifications of the other sections will be retained but without possibility of recovery.
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 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 10 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 20 |
| Final test [PRESENCIAL][Assessment tests] | 2 |
| Unit 1 (de 6): Introduction to Multivariate Analysis | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Class Attendance (practical) [PRESENCIAL][Other Methodologies] | 3 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 4 |
| Other on-site activities [PRESENCIAL][Workshops and Seminars] | 1 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 4 |
| Unit 2 (de 6): Analysis of variance | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Class Attendance (practical) [PRESENCIAL][Other Methodologies] | 3 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 4 |
| Other on-site activities [PRESENCIAL][Workshops and Seminars] | 2 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 4 |
| Unit 3 (de 6): Data Reduction Methods | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4.5 |
| Class Attendance (practical) [PRESENCIAL][Other Methodologies] | 4.5 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 6 |
| Other on-site activities [PRESENCIAL][Workshops and Seminars] | 3 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 6 |
| Unit 4 (de 6): Clasification and Comparison of Groups | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Class Attendance (practical) [PRESENCIAL][Other Methodologies] | 3 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 4 |
| Other on-site activities [PRESENCIAL][Workshops and Seminars] | 2 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 4 |
| Unit 5 (de 6): Models for Qualitative Data Analysis | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4.5 |
| Class Attendance (practical) [PRESENCIAL][Other Methodologies] | 4.5 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 6 |
| Other on-site activities [PRESENCIAL][Workshops and Seminars] | 2 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 6 |
| Unit 6 (de 6): Other Techniques for Business Data Analysis | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4.5 |
| Class Attendance (practical) [PRESENCIAL][Other Methodologies] | 4.5 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 6 |
| Other on-site activities [PRESENCIAL][Workshops and Seminars] | 3 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 6 |
| Global activity | |
|---|---|
| Activities | hours |
10. Bibliography and Sources
| Author(s) | Title | Book/Journal | Citv | Publishing house | ISBN | Year | Description | Link | Catálogo biblioteca |
|---|---|---|---|---|---|---|---|---|---|
| Arriaza, Fernández, López, Muñoz, ... | Estadística Básica con R y R-Commander | Universidad de Cádiz | |||||||
| Baillo Moreno, Amparo | 100 problemas resueltos de estadística multivariante : (impl | Madrid | Delta | 84-96477-73-8 | 2007 |
|
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| Grant, E.L. | Control estadístico de calidad | Compañía Editorial Continental | 968-26-1256-X | 2004 |
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| Hair, J.F., Anderson, R.E., Tatham, R.L. y Black, W.C. | Análisis multivariante | Madrid | Prentice Hall | 978-84-8322-035-1 | 2005 |
|
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| Johnson, Richard Arnold | Applied multivariable statistical analysis | Prentice Hall | 0-13-834194-X | 1998 |
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| Kline, Rex B. | Principles and practice of structural equation modeling | Guilford Press, | 978-1-4625-2334-4 | 2016 |
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| Lévy, J.P. y Varela, J. (dirs) | Análisis multivariable para las ciencias sociales | Madrid | Pearson Education | 978-84-205-3727-6 | 2008 |
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| Mitra, Amitava | Fundamentals of Quality Control and Improvement | Upper Saddle River, NJ | Prentice-Hall | 0-13-645086-5 | 1998 |
|
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| Monecke, A. & Leisch, L. | semPLS: Structural Equation Modeling Using Partial Least Squares | 2012 | https://www.jstatsoft.org/article/view/v048i03 | ||||||
| Montgomery, D.C. | Introduction to statistical quality control | Wiley | 0-471-66122-8 | 2005 |
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| Mulaik, Stanley A.1935- | Linear causal modeling with structural equations | CRC Press | 978-1-4398-0038-6 | 2009 |
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| Peña, D. | Análisis de datos multivariantes | McGraw-Hill | 8448136101 | 2002 | |||||
| Pérez López, César | Control estadístico de la calidad : teoría, práctica y apli | RA-MA | 84-7897-331-1 | 1998 |
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| Pérez López, César | Técnicas de análisis multivariante de datos | Pearson Educación | 978-84-205-4104-4 | 2008 |
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| Rosseel, Y. | lavaan: An R Package for Structural Equation Modeling | 2012 | https://www.jstatsoft.org/article/view/v048i02 | ||||||
| Vicente y Oliva, María A. de | Análisis multivariante para las ciencias sociales | Dykinson Universidad Rey Juan Carlos | 84-8155-541-X | 2000 |
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