Teaching plan for the course unit

 

 

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General information

 

Course unit name: Statistics I

Course unit code: 363647

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

(With the exception of the GIE group.)

 

-  Lecture with practical component

Face-to-face

 

45

 

-  Problem-solving class

Face-to-face

 

15

Supervised project

40

Independent learning

50

 

 

Recommendations

 

Students are advised to complete Mathematics I and Mathematics II before taking this subject.

 

 

Competences / Learning outcomes to be gained during study

 

   -

Capacity for learning and responsibility (capacity for analysis and synthesis, to adopt global perspectives and to apply the knowledge acquired/capacity to take decisions and adapt to new situations).

   -

To be able to use ICT in professional practice.

   -

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.

Learning objectives

 

Referring to knowledge

Learn to interpret the results of descriptive analysis, taking into account its limitations.

 

Learn to identify the statistical techniques used to summarise information extracted from empirical data and to aid decision-making in business settings.

 

Learn to incorporate the concepts of probability and stochastic modelling into the resolution of problems requiring identification of the most suitable model for describing a statistical population, based on previously prepared sample information.

 

Acquire basic knowledge of the statistical techniques used to address decision-making problems in a professional environment.

 

Learn to identify the types of information to which statistical analysis is applicable.

 

Referring to abilities, skills

Acquire the ability to effectively manage information derived from frequency tables, selecting the most appropriate graphic representation and summarising data to obtain relevant descriptive statistics.

 

 

Teaching blocks

 

1. Concepts and the subject of statistics

1.1. Descriptive statistics and statistical inference

1.2. Population and samples

1.3. Data classification

1.4. Introduction to R Commander

2. Frequency distribution and graphic representation

2.1. Frequency distribution

2.2. Graphical representations

3. Summary measures

3.1. Measures of position

3.2. Measures of dispersion

3.3. Measures of shape

4. Two-dimensional frequency distribution

4.1. Joint distributions; statistical independence

4.2. Measures of association; covariance and correlation

4.3. Linear regression

5. Probability theory

5.1. Introduction to probability: random experiment; axiomatic and properties

5.2. Conditional probability

5.3. Intersection theorem; statistical independence

5.4. Law of total probability and Bayes’ law

6. Random variable

6.1. The concept of random variable: discrete and continuous random variables

6.2. Probability distribution: quantity function and density function

6.3. Distribution function

6.4. Characteristics of random variables: mathematical expectation and variance

7. One-dimensional probability distributions

7.1. Discrete probability distribution

7.2. Continuous probability distribution

7.3. Convergence: central limit theorem

 

 

Teaching methods and general organization

 

The teaching methodology for this subject emphasises face-to-face work and distance learning and comprises a range of activities:

1. Face-to-face theory classes, in which the main concepts of the subject are explained, and various practical examples are used to relate and assimilate newly acquired knowledge.

2. Face-to-face problem-solving practical classes, devoted to work on the concepts introduced throughout the course. Exercises and tests are set to be completed and submitted, and databases of social, economic and business information are provided. The R Commander application is used for database work. The aim of these sessions is to help students develop their ability to manage, summarise and interpret data. They take the form of tutored sessions directed by the teaching staff and represent a significant proportion of the face-to-face activities for continuous assessment.

Additionally, students are expected to complete exercises and practical work independently, outside class, which also forms part of the continuous assessment. The following supplementary material is available on the Virtual Campus:

  • Online tests with answer sheets, by topic.
  • Suggestions for practical activities.
  • Model solutions and discussions of practical cases.
  • Summaries of the concepts and issues most relevant to each topic.


The GIE group follows a specific methodology. This group is for students who have already taken this subject. Teaching for this group is delivered in a 2-hour weekly session devoted to independent learning activities, during which students make intensive use of the Virtual Campus.

 

 

Official assessment of learning outcomes

 

Students may choose between two types of assessment: continuous assessment or single assessment. Students demonstrate that they have acquired a satisfactory understanding of the concepts and material covered during the course by obtaining at least a Pass grade.

Continuous assessment consists of:

— Activities set by the lecturer and carried out in face-to-face or online mode via the Virtual Campus (online questionnaires). The overall mark for these tasks represents 40% of the final grade. The exact dates will be announced at the beginning of the course via the Virtual Campus.

— A final examination set by the Academic Council in the standard academic calendar, which accounts for the remaining 60% of the final grade. The examination contains multiple-choice questions on the theoretical and applied content of the course.

For the marks for assessment tasks and the examination to be considered in the calculation of the final grade, students must complete a minimum of 80% of continuous assessment activities and obtain a mark of at least 3 in the final examination; otherwise, they are automatically entered for single assessment.

Students in the GIE group may request continuous assessment, subject to the criteria presented above.

Repeat assessment

Students are entitled to repeat assessment. This is a joint examination for students entered for continuous assessment and single assessment who have not achieved a pass grade for the course. Students sit a final multiple-choice examination on the theoretical and practical aspects of the course content. The examination is held on the date set by the Academic Council.

There will be no examinations outside of the official exam sittings.

 

Examination-based assessment

Students who do not wish to be assessed on a continuous basis are entered for single assessment, which consists of a final examination worth 100% of the final grade for the subject. The examination date is set by the Academic Council. This examination consists of multiple-choice questions on the theoretical and practical aspects of the whole course content.

Students in the GIE group may request single assessment, under the criteria described above.

Repeat assessment

Students are entitled to repeat assessment. This is a joint examination for students entered for continuous assessment and single assessment who have not achieved a pass grade for the course. Students sit a final multiple-choice examination on the theoretical and practical aspects of the course content. The examination is held on the date set by the Academic Council.

There will be no examinations outside of the official exam sittings.

 

 

Reading and study resources

Check availability in Cercabib

Book

ALEA RIERA, Victoria. Estadística I: cuestiones tipo test con R-Commander. Barcelona: Edicions UB, 2011

Catāleg UB  Enllaç
Versiķ en línia (2011)  Enllaç

ALEA RIERA, Victoria  et al . Guía para el análisis estadístico con R-Commander. Barcelona: Textos Docents 391 Edicions UB, 2014

Catāleg UB  Enllaç

ALEA RIERA, Victoria  Estadística para las ciencias sociales: cuestiones tipo test. Madrid: AC, Alfa Centauro, 2002

Catāleg UB  Enllaç

LIND, Douglas A. Estadística aplicada a los negocios y a la economía. 16ª ed. México: McGraw-Hill, 2015

Catāleg UB  Enllaç

MARTIN GUZMÁN, Pilar. Manual de estadística descriptiva. Cizur Menor: Aranzadi, 2006

Catāleg UB  Enllaç

MARTÍN-PLIEGO LÓPEZ, Fco.Javier. Introducción a la estadística económica y empresarial: teoría y práctica. 3a ed. Madrid: AC, Alfa Centauro, 2011

Catāleg UB  Enllaç

MONTIEL TORRES, Ana María. Elementos básicos de estadística económica y empresarial. Madrid: Prentice-Hall Int., 2002

Catāleg UB  Enllaç

NEWBOLD, Paul. Estadística para los negocios y la economía. Madrid: Prentice-Hall, 1998

Catāleg UB  Enllaç

PEÑA, Daniel; ROMO, Juan. Introducción a la estadística para las ciencias sociales. Madrid: McGraw-Hill Int., 2003

Catāleg UB  Enllaç

PÉREZ LÓPEZ, César. Estadística aplicada a través de Excel. Madrid: Prentice Hall, 2011

Catāleg UB  Enllaç

Web page

Instituto Nacional de Estadística [en línia] Madrid [Consulta: 2 de juny de 2017]. Disponible a: http://www.ine.es

Institut d’Estadística de Catalunya.Idescat.[en línia] Barcelona [Consulta: 2 de juny de 2017]. Disponible a: http://www.idescat.cat

Banco de España [en línia] Madrid [Consulta: 2 de juny de 2017]. Disponible a: http://www.bde.es

ALEA, V., VILADOMIU, N.[en línea] Barcelona [Consulta: 2 de juny de 2017]. Disponible a: http://www.ub.edu/dpees/

Electronic text

ALEA, V., GUILLÉN, M., MUÑOZ, MC., TORRELLES; E., VILADOMIU, N.  CD- Estadística descriptiva básica. Edicions Universitat de Barcelona. 2001