Academic Year/course:
2023/24
525 - Master's in Economics
61343 - Multivariate Analysis Techniques
Syllabus Information
Academic year:
2023/24
Subject:
61343 - Multivariate Analysis Techniques
Faculty / School:
109 - Facultad de Economía y Empresa
Degree:
525 - Master's in Economics
ECTS:
3.0
Year:
1
Semester:
Second semester
Subject type:
Optional
Module:
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1. General information
The approach of the subject is markedly instrumental since its objective is to provide the student with a set of statistical tools widely used in the performance of a multidimensional exploratory analysis. All topics will be approached from a practical point of view, using different data sets to illustrate the techniques explained. For this purpose, the R programming language and environment will be used, which integrates a multitude of packages that increase its capacity and versatility.
These approaches and objectives are directly aligned with the Sustainable Development Goals (SDGs) of the United Nations Agenda 2030 (https://www.un.org/sustainabledevelopment/es/) since the examples worked on in class analyse databases that address these issues. Specifically, they are aligned with Goal 7 (Affordable and Clean Energy), Goal 11 (Sustainable Cities and Communities), Goal 12 (Responsible Production and Consumption) and Goal 13 (Climate Action). However, all the training provided by this subject (theoretical and practical) contributes transversally to AGENDA 2030 and SDGs since it enables the student to contribute to the analysis and management of the 245 indicators of the SDGs. The acquisition of the learning results of the subject provides training and competence to contribute to some extent to their achievement.
2. Learning results
-To perform an initial analysis of a multivariate data set.
-To perform a principal component analysis and interpret the results obtained.
-To perform a factor analysis and interpret the results obtained.
-To carry out a data classification process using agglomerative hierarchical procedures
-To carry out a data classification process using partitioning procedures.
-To design classification procedures in order to discriminate between groups of observations.
-To validate and interpret the results obtained in a classification procedure.
3. Syllabus
Topic 0: Introduction to R
Topic 1: Initial data analysis
Topic 2: Principal component analysis
Topic 3: Factor analysis
Topic 4: Cluster analysis
Topic 5: Discriminant analysis
4. Academic activities
Lectures: 15 hours
Practical classes: 15 hours
Personal study: 45 hours
5. Assessment system
The assessment will be continuous. For this purpose, the following will be carried out:
- 6 deliveries of a test-type questionnaire for each of the topics, except for topic 1, where there will be a questionnaire for the one-dimensional part and another for the two-dimensional part (30% of the final grade as long as its average is higher than 3).
- 1 report including the solving of exercises related to an exploratory one-dimensional and two-dimensional analysis applied to a database (30% of the final grade provided that the grade of the report is higher than 3.)
- 1 report including the solving of exercises of application of the multivariate techniques
studied in the subject (40% of the final grade provided that the grade of the report is higher than 3).
If the student has not passed any of these activities during the term, they will have the opportunity to pass it by means of a global test in the two official calls for exams.