Teaching plan for the course unit

 

 

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General information

 

Course unit name: Applied Statistics I

Course unit code: 363689

Academic year: 2025-2026

Coordinator: Francisco Javier Sierra Martinez

Department: Department of Econometrics, Statistics and Applied Economics

Credits: 6

Single program: S

 

 

Estimated learning time

Total number of hours 150

 

Face-to-face and/or online activities

60

 

-  Lecture with practical component

Face-to-face

 

15

 

-  IT-based class

Face-to-face

 

45

Supervised project

40

Independent learning

50

 

 

Recommendations

 

Students are recommended to have completed the core courses in Mathematics, Statistics, and Business Econometrics to be able to understand the concepts introduced in Applied Statistics I.

 

 

Competences / Learning outcomes to be gained during study

 

   -

To be able to use ICT in professional practice.

   -

To be able to make financial and business decisions, taking into account the current economic situation.

   -

To use basic quantitative methods and instruments to obtain and analyse company information and its socioeconomic environment, in accordance with the characteristics of the available information.

   -

To draw up and develop a marketing plan on the basis of an in-depth analysis of each phase.

Learning objectives

 

Referring to knowledge

The course outlines the problems of analysing large databases. The interdependence between the variables that make up a database are analysed, and an introduction is provided to the basic elements of multivariate analysis, which is essential for following this course in applied statistics.

The multivariate models analysed can be grouped into two major techniques: classification and data reduction techniques. In relation to the first group, we consider discriminant analysis and cluster analysis, and groups of cases are defined. In relation to the second group, the principal component model, factor analysis and correspondence analysis are analysed in depth.

 

 

Teaching blocks

 

1. Introduction: basic elements of multivariate analysis

1.1. Data. Classification and scales of measurement

1.2. Basic formulation and hypotheses of multivariate analysis

1.3. Description and synthesis of a data matrix

1.4. Covariance and correlation coefficient

1.5. Distance matrix

2. Principal components

2.1. Dimensionality reduction of a data array

2.2. Obtaining the components

2.3. Interpretation of results. Perceptual mapping

3. Factor analysis

3.1. Objectives of factor analysis

3.2. Model formulation: basic hypotheses

3.3. Model estimation

3.4. Interpretation of factors

4. Correspondence analysis

4.1. Objectives of simple correspondence analysis

4.2. Marginal and conditional models

4.3. Biplots: interpretation of results

4.4. Introduction to multiple correspondence analysis

5. Discriminant analysis

5.1. Formulation and objectives of the analysis

5.2. Elements of the analysis

5.3. Contrasts of differences between groups

5.4. Variables and canonical space

5.5. Classification functions

6. Cluster analysis

6.1. Object of analysis

6.2. Distances and dissimilarities

6.3. Hierarchical methods

6.4. Optimization methods: AID and CHAID

 

 

Teaching methods and general organization

 

Since the course has a strong applied focus, the methodology combines the study of theoretical concepts with their practical application. Teaching is organised into:

— Theoretical learning activities: delivered in class with supporting materials provided on the Virtual Campus.

— Practical learning activities: classroom-based problem solving and analysis, using examples explained on the board or through R-Studio software.

— Independent learning activities: exercises using R-Studio software to apply theoretical knowledge to problems that simulate real-world situations, with guidance from teaching staff where needed.

 

 

Official assessment of learning outcomes

 

Students may choose between two modes of assessment: continuous and single. To demonstrate sufficient knowledge, they must obtain at least a passing grade.

Continuous assessment consists of:

— Activities set by the teaching staff, to be completed both in and outside the classroom via the Virtual Campus. The combined weighting of these activities accounts for 40% of the final grade At the start of the course, the dates for completing the various activities are communicated via the Virtual Campus.

— A final exam, scheduled by the Academic Board, which accounts for 60% of the final grade. The exam consists of both theoretical and practical questions, which must be completed using the R-Studio software.

For both parts of the continuous assessment to be taken into consideration, students must complete at least 80% of the continuous assessment activities and obtain a minimum score of 3 on the final exam; otherwise, they are automatically transferred to the single mode of assessment.

The repeat assessment of the subject follows the same procedure as that for the single mode. No examinations are scheduled outside the official exam periods. 

 

Examination-based assessment

To pass the course, students must demonstrate a sufficient level of attainment of the course objectives. They demonstrate this by obtaining the minimum passing grade on the written final exams in the official exam sessions. Given the theoretical and empirical nature of the course, these exams assess both aspects. Specifically, assessment is conducted through a problem-based exam, which students must complete using R-Studio software.

The repeat assessment of the subject follows the same procedure as the single assessment. No examinations are scheduled outside the official exam periods.

 

 

Reading and study resources

Check availability in Cercabib

Book

ALUJA, Tomàs, et al. Aprender de los datos: el análisis de componentes principales: una aproximación desde el Data Mining. Barcelona: EUB, 1999

Catāleg UB  Enllaç

LUQUE MARTINEZ, T. Técnicas de análisis de datos en investigación de mercados. Madrid: Pirámide, 2012

Catāleg UB  Enllaç

PEÑA, D. Análisis de datos multivariantes. Madrid: McGraw-Hill, 2002

Catāleg UB  Enllaç

PÉREZ LÓPEZ, C. Métodos estadísticos avanzados con SPSS. Madrid: Thomson, 2005

Catāleg UB  Enllaç

PÉREZ LÓPEZ, C. Técnicas de análisis multivariante de datos: aplicaciones con SPSS. Madrid: Prentice Hall, 2011

Catāleg UB  Enllaç

URIEL JIMÉNEZ, E. Análisis de datos: series temporales y análisis multivariante. Madrid: AC, 1995

Catāleg UB  Enllaç

URIEL JIMÉNEZ, E. Análisis multivariante aplicado: aplicaciones al marketing, investigación de mercados, economía, dirección de empresas y turismo. Madrid: Thomson, 2005

Catāleg UB  Enllaç

JOAQUIM ALDÁS y  EZEEQUIEL URIEL. Análisis Multivariante Aplicado con R, Editorial Paraninfo,2017

Versiķ en línia (2a ed., 2017)  Enllaç