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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
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Estimated learning time |
Total number of hours 150 |
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Face-to-face and/or online activities |
60 |
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(With the exception of the GIE group.) |
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- Lecture with practical component |
Face-to-face |
45 |
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- Problem-solving class |
Face-to-face |
15 |
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Supervised project |
40 |
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Independent learning |
50 |
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Recommendations |
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Students are advised to complete Mathematics I and Mathematics II before taking this subject. |
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Competences / Learning outcomes to be gained during study |
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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). |
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To be able to use ICT in professional practice. |
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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. |
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Learning objectives |
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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. |
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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
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Teaching methods and general organization |
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The teaching methodology for this subject emphasises face-to-face work and distance learning and comprises a range of activities: |
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Official assessment of learning outcomes |
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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.
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. |
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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
Versiķ en línia (2011)
ALEA RIERA, Victoria et al . Guía para el análisis estadístico con R-Commander. Barcelona: Textos Docents 391 Edicions UB, 2014
ALEA RIERA, Victoria Estadística para las ciencias sociales: cuestiones tipo test. Madrid: AC, Alfa Centauro, 2002
LIND, Douglas A. Estadística aplicada a los negocios y a la economía. 16ª ed. México: McGraw-Hill, 2015
MARTIN GUZMÁN, Pilar. Manual de estadística descriptiva. Cizur Menor: Aranzadi, 2006
MARTÍN-PLIEGO LÓPEZ, Fco.Javier. Introducción a la estadística económica y empresarial: teoría y práctica. 3a ed. Madrid: AC, Alfa Centauro, 2011
MONTIEL TORRES, Ana María. Elementos básicos de estadística económica y empresarial. Madrid: Prentice-Hall Int., 2002
NEWBOLD, Paul. Estadística para los negocios y la economía. Madrid: Prentice-Hall, 1998
PEÑA, Daniel; ROMO, Juan. Introducción a la estadística para las ciencias sociales. Madrid: McGraw-Hill Int., 2003
PÉREZ LÓPEZ, César. Estadística aplicada a través de Excel. Madrid: Prentice Hall, 2011
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