ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 24 Interactive Classroom: 26 Total: 51
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Quantitative Economy
Areas: Quantitative Economics (USC-specific)
Center Faculty of Economics and Business Studies
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
-Provide students with the knowledge and management of the statistical techniques necessary for the descriptive and inferential analysis of economic and business data as well as the methodology of the main statistics that we have.
-Perform basic statistical analyzes using computer tools.
-Be able to select and apply the most appropriate statistical techniques to the analysis of each phenomenon, contextualize the results obtained and interpret them.
- Get started in the analysis of empirical data of the economic and business reality (data collection, statistical analysis, interpretation and reliability of the results, etc.) as well as in the formal presentation of the work.
- Develop the skills of comprehension, reasoning, criticism and oral and written expression, through the use of appropriate statistical-economic-business lexicon.
TOPIC 1.- ECONOMIC STATISTICS
1.1.- Basic concepts
1.2.- Sources of statistical data
TOPIC 2.- DESCRIPTIVE ANALYSIS OF VARIABLE DUNHA
2.1.- Frequency distributions. Graphic representations
2.2.- Summary measurements: position, dispersion and shape
TOPIC 3.- CONCENTRATION AND INEQUALITY
3.1.- Concentration
3.2.- Inequality. Welfare.
TOPIC 4.- DESCRIPTIVE ANALYSIS OF TWO VARIABLES
4.1.- Two-dimensional frequency distributions. Graphic representations
4.2.- Marginal and conditional distributions.
4.3.- Statistical independence. Covariance
TOPIC 5.- CORRELATION and ASSOCIATION
5.1.- Linear correlation
5.2.- Rank Correlation
5.3.- Association of variables
TOPIC 6.- INDEX NUMBERS
6.1.- Variation rates. Index numbers.
6.2.- Price, quantity and value index numbers
6.3.- Base change. Deflation.
TOPIC 7.- SOCIOECONOMIC STATISTICS
7.1.- Consumer Price Index
7.2.- Production index
7.3.- Active Population Survey. Registered unemployment.
7.4.- Financial and stock market statistics.
7.5.- Other economic indicators.
TOPIC 8.- PROBABILITY
8.1.- Probability assignment. Kolmogorov axiomatics.
8.2.- Conditional, joint and total probability. Independence
8.3.- Bayes' Theorem
TOPIC 9.- RANDOM VARIABLES
9.1.- Random variables. Probability distributions.
9.2.- Summary characteristics: expectation, variance,...
9.3.- Generalizations to more than one variable. Independence, covariance, correlation.
TOPIC 10.- DISCRETE AND CONTINUOUS PROBABILITY MODELS
10.1.- Binomial Distribution.
10.2.- Poisson distribution.
10.3.- Normal and bivariate Normal Distribution.
10.4.- Exponential Distribution
10.5.- Other distributions
Basic:
Esteban García, J. et al. (2008). Estadística Descriptiva y nociones de Probabilidad, Ed. Paraninfo
Martín Pliego, F.J. (2004). Introducción a la Estadística Económica y Empresarial: Teoría y práctica. Ed. Thompson Paraninfo S. A., Madrid.
Martín Pliego, F.J. e L. Ruiz-Maya. (2006). Fundamentos de Probabilidad . Ed. Thomson-Paraninfo.
Newbold, P. et al. (2008). Estadística para los Negocios y la Economía. Ed. Prentice-Hall.
Complementary:
Cao Abad, R. et at (2001). Introducción a la estadística y sus aplicaciones. Ed. Pirámide.
Casas Sánchez, J.M. e Santos Peñas, J. (2002), Introducción a la Estadística para Administración y Dirección de Empresas. Ed. Centro de Estudios Ramón Areces.
Casas Sánchez, J.M. et a. (2006) Ejercicios de estadística descriptiva y probabilidad para Economía y Administración de Empresas, Ed. Pirámide
Durá Peiró, J.M. e J.M. López Cuñat. (1989): Fundamentos de Estadística. Estadística descriptiva y Modelos Probabilísticos para la Inferencia. Ed. Ariel.
Fernández- Abascal, H., M. Guijarro, J.L. Rojo, J.A. Sanz. (1994). Cálculo de Probabilidades y Estadística. Ed. Ariel.
Levin, R.I., Rubin, D.S (2007) Estadística para Administración y Economía, Ed. Pearson Eduacion. Prentice Hall
Lind, D.A.; Marchal, W.G. e Wathen, S.A. (2015): Estadística Aplicada a los Negocios y a la Economía. Ed. McGrawHill.
Martín Pliego, F.J., Montero Lorenzo, J.M. e Ruiz-Maya L. (2006): Problemas de probabilidad. Ed. Thompson Paraninfo.
Peña. D. (2014). Fundamentos de Estadística. Ed. Alianza Editorial
Pérez Suárez,R. et al. (1993). Análisis de datos económicos 1. Métodos Descriptivos. Pirámide, Madrid.
C6. Interpret the fundamental quantitative tools and techniques (mathematics, statistics, econometrics) for economic-business diagnosis, analysis and prospecting and for decision making.
HD1. Critically examine data from various sources in order to gain additional knowledge and use it to solve problems and make decisions.
HD2. Communicate orally and/or in writing effectively, with precision and clarity, with the purpose of transmitting knowledge, methodologies, data, results, difficulties and solutions.
HD7. Handle different mathematical, statistical, econometric techniques and computer programs for the visualization, analysis and modeling of economic-business data.
HD8. Manage computer programs and tools for analysis and decision making in the scope of the different functional areas of business.
CP8. Generate studies and reports from the analysis and modeling of economic-business data, using mathematical, statistical and econometric techniques and tools.
Expository classes: Presentation, introduction and explanation of the basic contents of the subject to be studied, the theoretical bases and the guidelines for solving practical cases and exercises in a way that allows adequate development of autonomous learning.
Laboratory classes: Resolution of problems and questions and application of statistical techniques to the analysis of economic reality, differentiating what type of technique to use in each case, how to apply it and what conclusions are obtained from the analysis carried out. Activities will be organized seeking or requiring the active participation of the student (discussion of cases, practices, problem solving, work with data, computer practices...). Problems and practical activities will be proposed; students must solve individually and/or in small groups. Computer programs will be used, complementing the training with computer practices, with the aim of acquiring skills in managing spreadsheets or other statistical packages useful for data analysis. Students will work individually or in groups.
Tutorials: Continuous advice to students for the development of the proposed autonomous activities. Personalized attention to students to resolve doubts related to the subject. It will be mainly in person, but it can also be done electronically through institutional email.
Virtual Classroom: Information on the subject, classroom presentations and support materials available to students for the course and preparation of the subject.
Scheduled tasks and tests:
Taking computer practices as a reference, continuous evaluation activities may consist of the statistical analysis of empirical phenomena, with real databases, to be carried out throughout the semester, using computer tools. These tasks are intended that the student acquires skills to carry out statistical analysis of empirical reality, learning to select and apply statistical techniques appropriate to each phenomenon, to use computer tools for analysis, to correctly interpret the results obtained, as well as to present/defend the work carried out using the lexicon of the discipline. Tests can also be carried out to evaluate the content developed and its understanding, individually or in groups.
The student will be evaluated with a continuous evaluation system. This system will use instruments to measure the continuous learning of the statistical concepts and methodologies of this subject, as well as their application to empirical reality. It will be based fundamentally on tasks in which the students show the level of knowledge acquired. The continuous evaluation system requires regular attendance at academic activities and active participation.
1st opportunity
Evaluation instruments and their weight in the final grade:
- Final exam: 70% of the total grade (7 points)
- Continuous evaluation activities: 30% of the total grade (3 points). Carrying out computer exercises/tests, solving exercises and/or group work.
2nd opportunity
The continuous evaluation score obtained throughout the course will be maintained and added to that of the final exam.
The student will not have the possibility of recovering the previous tasks, activities and tests pending completion by the students linked to the continuous evaluation or not delivered in a timely and/or manner.
The continuous evaluation of students will be based on the tasks and/or tests carried out or assigned during the course, in groups or individually, and their active participation in academic activities. Its distribution within the total will be: content surveys (10%), assigned and delivered tasks and active participation (10%), tests linked to the use of computer programs (10%). The different evaluable activities throughout the course will be proposed and carried out in face-to-face sessions and/or collected in the face-to-face or virtual classroom.
The final individual test will be carried out on the dates established by the Center.
In order to take the exams it will be necessary to present an official identification document (DNI, Passport...).
Attendance at activities will be governed by the USC Regulamento de asistencia a clase nas ensinanzas de grao e máster. The evaluation in cases of exemption from class attendance will be carried out with the final test valued on the highest possible grade.
According to the Permanence regulations in force at the USC for Degree's and Master's studies (art. 5.2), mere attendance and/or participation in any of the activities subject to evaluation will mean that the student's final grade is different from Not Presented.
In cases of fraudulent completion of exercises or tests, the provisions of the “Normativa de avaliación do rendomento académico dos estudiantes e de revisión de cualificacións” will apply.
EVALUATION OF COMPETENCES-LEARNING RESULTS
Continuous Evaluation: C6, HD1, HD2, HD7, HD8, CP8
Evaluation by Exam: C6, HD2, HD7, CP8
Following the report of the Degree:
Expository teaching 24 hours
Laboratory teaching 26 hours
Tutorials 1h
Total personal student work hours 99 hours
Carlos Pio Del Oro Saez
Coordinador/a- Department
- Quantitative Economy
- Area
- Quantitative Economics (USC-specific)
- carlospio.deloro [at] usc.es
- Category
- Professor: University Lecturer
Angela Troitiño Cobas
- Department
- Quantitative Economy
- Area
- Quantitative Economics (USC-specific)
- Phone
- 881811556
- angela.troitino [at] usc.es
- Category
- Professor: University Lecturer
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| 11:00-12:30 | Grupo /CLE_02 | Galician | Classroom B |
| 12:30-14:00 | Grupo /CLE_01 | Galician | Classroom A |
| Thursday | |||
| 09:30-11:00 | Grupo /CLE_02 | Galician | Classroom B |
| 18:00-19:30 | Grupo /CLE_03 | - | Classroom A |
| Friday | |||
| 12:30-14:00 | Grupo /CLE_01 | Galician | Classroom A |
| 18:00-19:30 | Grupo /CLE_03 | - | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_1a | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_3a | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLE_02 | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_5a | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_1b | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_3b | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_5b | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLE_03 | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_2 | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLE_01 | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_6 | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_4 | Classroom A |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_1b | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_3b | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLE_01 | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_5b | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLE_03 | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_2 | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_6 | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_4 | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_1a | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_3a | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLE_02 | Classroom B |
| 06.05.2026 16:00-19:00 | Grupo /CLIL_5a | Classroom B |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_2 | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_6 | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_4 | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_1a | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_3a | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLE_01 | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLE_02 | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_5a | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_1b | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_3b | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_5b | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLE_03 | Classroom A |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_1a | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_3a | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_5a | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLE_02 | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_1b | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_3b | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_5b | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLE_03 | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_2 | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLE_01 | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_6 | Classroom C |
| 07.09.2026 09:00-12:00 | Grupo /CLIL_4 | Classroom C |