Checking date: 08/06/2021


Course: 2021/2022

Multivariate Analysis II
(13727)
Study: Bachelor in Statistics and Business (203)


Coordinating teacher: GALEANO SAN MIGUEL, PEDRO

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Exploratory Data Analysis Elemental Statistical Theory I Elemental Statistical Theory II Statistical Inference Methods I Statistical Inference Methods II Mathematical Methods I Mathematical Methods II Advanced Mathematical Methods I Advanced Mathematical Methods II Multivariate Analysis Regression Analysis
Objectives
COMPETENCES 1. Acquire skills in dimension reduction techniques such as factor analysis, multidimensional scaling and the correspondence analysis. 2. Acquire skills in heterogeneity problems such as clustering. 3. Capacity for analyzing dependency between multivariate variables by means of multivariate regression and canonical correlation analysis. 4. Know marketing and financial applications of multivariate techniques. 5. Handle statistical software for multivariate analysis. SKILLS 1. Aptitude to understand a real problem and to analyze it as an statistical problem. 2. Modeling and solving problems. 3. Capacity of analysis and synthesis. 4. Oral and written skills. 5. Aptitude to work in a group.
Description of contents: programme
1. Introduction. 1.1 Where do we come from? 1.2 Whre do we go? 2. Cluster analysis. 2.1 Introduction. 2.2 Partition methods. 2.3 Hierarchical methods. 3. Multidimensional Scaling. 3.1 Introduction. 3.2 Distances, proximities and dissimilarities. 3.3 Metric multidimensional scaling. 4. Factor analysis. 4.1 Introduction. 4.2 The factor model. 4.3 Estimation of the factor model parameters. 4.4 Rotations in the factor model. 4.5 Factor model scores. 4.6 Alternative procedures. 5. Multivariate regression. 5.1 Introducition. 5.2 Univariate regression. 5.3 Multivariate regression. 6. Canonical correlation analysis. 6.1 Introduction. 6.2 Canonical correlations.
Learning activities and methodology
Theory (4 ECTS): Theoretical classes with support material taken from the web. Practical classes (2 ECTS): Problem solving classes. Computing classes in computer halls. Work assignments in groups. Oral presentations and debates. Tutorial classes before the midterm exams. Tutorial classes during the week 15.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • Richard A. Johnson and Dean W. Wichern. Applied Multivariate Statistical Analysis. Pearson Education. 2007

The course syllabus may change due academic events or other reasons.