Guías Docentes Electrónicas
1. General information
Course:
TÉCNICAS ESTADÍSTICAS APLICADAS A LA EMPRESA
Code:
54336
Type:
ELECTIVE
ECTS credits:
6
Degree:
317 -
Academic year:
2019-20
Center:
5 -
Group(s):
12 
Year:
4
Duration:
First quarter
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
ECO.POL/ HAC. PUB.,EST.ECO./EMP Y POL EC
2176
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 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 Ficha de la biblioteca
Grant, E.L. Control estadístico de calidad Compañía Editorial Continental 968-26-1256-X 2004 Ficha de la biblioteca
Hair, J.F., Anderson, R.E., Tatham, R.L. y Black, W.C. Análisis multivariante Madrid Prentice Hall 978-84-8322-035-1 2005 Ficha de la biblioteca
Johnson, Richard Arnold Applied multivariable statistical analysis Prentice Hall 0-13-834194-X 1998 Ficha de la biblioteca
Kline, Rex B. Principles and practice of structural equation modeling Guilford Press, 978-1-4625-2334-4 2016 Ficha de la biblioteca
Lévy, J.P. y Varela, J. (dirs) Análisis multivariable para las ciencias sociales Madrid Pearson Education 978-84-205-3727-6 2008 Ficha de la biblioteca
Mitra, Amitava Fundamentals of Quality Control and Improvement Upper Saddle River, NJ Prentice-Hall 0-13-645086-5 1998 Ficha de la biblioteca
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 Ficha de la biblioteca
Mulaik, Stanley A.1935- Linear causal modeling with structural equations CRC Press 978-1-4398-0038-6 2009 Ficha de la biblioteca
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 Ficha de la biblioteca
Pérez López, César Técnicas de análisis multivariante de datos Pearson Educación 978-84-205-4104-4 2008 Ficha de la biblioteca
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 Ficha de la biblioteca



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