1. General information
Course:
STATISTICAL DATA ANALYSIS
Code:
53341
Type:
ELECTIVE
ECTS credits:
4.5
Degree:
316 - UNDERGRADUATE DEGREE IN ECONOMICS
Academic year:
2022-23
Center:
5 - FACULTY OF ECONOMICS AND BUSINESS
Group(s):
10
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):
10
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
2. Pre-Requisites
It is recommended to have coursed the subjects on Statistics for Economics 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 |
| E03 | Ability to find economic data and select relevant facts. |
| E06 | Application of profesional criteria to the analysis of problems, based on the use of technical tools. |
| E11 | Diagnosis and assessment skills to conduct structural and cyclical reports, as well as economic forecast summaries on the reality of the economy in Spain, the European Union and in any of the product sectors and factor markets. To do so, it will be necessary to understand and use common handbooks, as well as articles and, in general, leading edge bibliography in the core subjects of the curriculum. |
| 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 for the use and development of information and communication technology in the development of professional activity. |
| G05 | Capacity for teamwork, to lead, direct, plan and supervise multidisciplinary and multicultural teams in both national and international environments. |
5. Objectives or Learning Outcomes
| Course learning outcomes | |
|---|---|
| Description | |
| Train the student to work out problems in creative and innovative ways. | |
| Train the student to listen to and defend arguments orally or in writing | |
| 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 1.1: Data and measurement scale
-
Unit 1.2: Introduction to Data Mining and software R for statistical computing
-
Unit 1.3: Descriptive and exploratory data analysis
-
Unit 1.4: Detection of outliers
-
Unit 1.5: Treatment of non-response
-
-
Unit 2: Clasification and comparison of groups
-
Unit 2.1: Linear discriminant analysis
-
Unit 2.2: Cluster Analysis
-
Unit 2.3: Analysis of variance
-
-
Unit 3: Data reduction methods
-
Unit 3.1: Principal component analysis
-
Unit 3.2: Factor analysis
-
-
Unit 4: Models for qualitative data analysis
-
Unit 4.1: Contingency tables and measures of association
-
Unit 4.2: Correspondence factor analysis
-
Unit 4.3: Multidimensional scaling
-
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 | E03 E06 E11 G04 | 1 | 25 | N | N | ||
| Class Attendance (practical) [ON-SITE] | Combination of methods | E03 E06 E11 G01 G03 G04 G05 | 0.5 | 12.5 | N | N | ||
| Study and Exam Preparation [OFF-SITE] | Self-study | E03 E06 E11 G01 G04 | 1.2 | 30 | N | N | ||
| Writing of reports or projects [OFF-SITE] | Group Work | E03 E06 E11 G01 G04 G05 | 0.86 | 21.5 | Y | N | ||
| Other off-site activity [OFF-SITE] | Self-study | E11 G01 G03 G04 | 0.76 | 19 | Y | N | ||
| Other on-site activities [ON-SITE] | Combination of methods | E06 E11 G01 G03 G04 G05 | 0.1 | 2.5 | N | N | ||
| Final test [ON-SITE] | Assessment tests | E06 E11 G01 G03 G04 | 0.08 | 2 | Y | Y | ||
| Total: | 4.5 | 112.5 | ||||||
| Total credits of in-class work: 1.68 | Total class time hours: 42 | |||||||
| Total credits of out of class work: 2.82 | Total hours of out of class work: 70.5 | |||||||
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 active participation | 5.00% | 0.00% | The active attitude of the student will be assessed in the classroom. |
| Fieldwork assessment | 20.00% | 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 | 15.00% | 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 | 60.00% | 100.00% | The teacher will provide the student some tasks which will have to be solved and delivered at the end of each theme. |
| 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 subject follows an evaluation system based on the assessment of various training activities and an exam. The student is required to obtain a 4 (out of 10) in the final evaluation test to make an average with the grade obtained in the rest of the proposed training activities. Depending on the circumstances of the group, and prior agreement with the students, the final test may be replaced by increasing the weight of the part corresponding to field work and the resolution of problems or cases.
Those students who, although they have carried out evaluable activities, wish to be evaluated with the criteria of non-continuous evaluation must inform before the end of the class period.
Regarding the evaluation in case of illness or other special circumstances (mitigating rules) see article 6 of the Regulation of Evaluation of the Student of the University of Castilla-La Mancha. -
Non-continuous evaluation:The evaluation will consist of a single theoretical-practical test in which the skills of the subject will be evaluated. It will consist of questions of theoretical content and practices in R language.
In accordance with section b of point 4.2. of the 2022 Student Regulations, any student may switch to the non-continuous assessment modality, by the procedure established by the teacher, provided that they have not participated during the class teaching period in evaluable activities that together involve at least the 50% of the total evaluation of the subject. If a student has reached that 50% of assessable activities or if, in any case, the class period has ended, they will be considered in continuous assessment without the possibility of changing the assessment modality.
Regarding the evaluation in case of illness or other special circumstances (mitigating rules) see article 6 of the Regulation of Evaluation of the Student of the University of Castilla-La Mancha.
Specifications for the resit/retake exam:
There are no particularities.
Specifications for the second resit / retake exam:
The evaluation will be carried out on a single test being necessary a minimum score of 5 out of 10 to pass the subject
9. Assignments, course calendar and important dates
| Not related to the syllabus/contents | |
|---|---|
| Hours | hours |
| Other on-site activities [PRESENCIAL][Combination of methods] | 2.5 |
| Final test [PRESENCIAL][Assessment tests] | 2 |
| Unit 1 (de 4): Introduction to Multivariate Analysis | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 6.67 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 3.33 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 7.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 5.75 |
| Other off-site activity [AUTÓNOMA][Self-study] | 4 |
| Group 10: | |
| Initial date: 16-09-2019 | End date: 07-10-2019 |
| Unit 2 (de 4): Clasification and comparison of groups | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 5.83 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 2.91 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 7.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 5 |
| Other off-site activity [AUTÓNOMA][Self-study] | 5 |
| Group 10: | |
| Initial date: 07-10-2019 | End date: 29-10-2019 |
| Unit 3 (de 4): Data reduction methods | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 5.83 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 2.91 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 7.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 5 |
| Other off-site activity [AUTÓNOMA][Self-study] | 5 |
| Group 10: | |
| Initial date: 04-11-2019 | End date: 25-11-2019 |
| Unit 4 (de 4): Models for qualitative data analysis | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 6.67 |
| Class Attendance (practical) [PRESENCIAL][Combination of methods] | 3.35 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 7.5 |
| Writing of reports or projects [AUTÓNOMA][Group Work] | 5.75 |
| Other off-site activity [AUTÓNOMA][Self-study] | 5 |
| Group 10: | |
| Initial date: 26-11-2019 | End date: 17-12-2019 |
| Global activity | |
|---|---|
| Activities | hours |
10. Bibliography and Sources
