ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 Total: 150
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: First Semester
Teaching: With teaching
Enrolment: Enrollable
Statistical inference deals with techniques of great utility in the economic-business field, as it allows valid conclusions to be drawn about population behavior from sample data.
The basic objective of this subject is to:
- Know and understand the concepts and methods of probability and statistical inference, as well as their application to the analysis of economic and business phenomena.
- Carry out basic statistical analyses using computer tools.
- Be able to select and apply the statistical techniques best suited to the analysis of each phenomenon and to interpret the results obtained.
- Be initiated in the analysis of empirical data of economic and business reality (data collection, statistical analysis, interpretation and reliability of results, etc.) and in the formal presentation of the work carried out.
- Develop the skills of comprehension, reasoning, criticism, and oral and written expression, through the use of an appropriate statistical-economic lexicon
TOPIC 1 PROBABILITY
1.1 Theories of probability: Classical, frequentist, subjective and axiomatic theory. Kolmogorov's axioms
1.2 Conditional and join probability.
1.3 Independence of events.
1.4 Bayes' theorem.
TOPIC 2 PROBABILITY DISTRIBUTION
2.1 Random variables: discrete and continuous.
2.2 Probability distribution of a random variable
2.3 Characteristics associated with random variables. Expectation, moments dispersion.
2.4 Generalization to the two-dimensional and multidimensional case.
TOPIC 3 DISCRETE AND CONTINUOUS PROBABILITY MODELS
3.1 Binomial distribution.
3.2 Poisson distribution.
3.3 Normal and bivariate normal distributions.
3.4 Exponential distribution
3.5 Distributions derived from the normal. Other models.
TOPIC 4 STATISTICAL INFERENCE. POINT ESTIMATION
4.1 Statistical Inference: populations and random samples.
4.2 Statistics and estimators.
4.3 Properties of point estimators
4.4 Point estimation construction
4.5 The Central Limit Theorem. Sample distribution.
TOPIC 5 ESTIMATION
5.1 Interval estimation. Constructing confidence intervals.
5.2 One sample confidence intervals. Sample sizes.
5.3 Two sample confidence intervals. Sample sizes.
TOPIC 6 PARAMETRIC HYPOTHESIS TESTING
6.1 Basic concepts. Hypotheses. Critical and acceptance region. Errors. Contrast's Power.
6.2 Methodology of hypothesis testing. The p-value.
6.3 Parametric contrasts.
TOPIC 7 NON PARAMETRIC CONTRAST
7.1 Introduction to non-parametric inference
7.2 Goodness-of-fit tests
7.3 Contrasts of randomness and location
7.4 Association tests and homogeneity
Basic
Lind, D.A.; Marchal, W.G. e Wathen, S.A. (2015): Estadística Aplicada a los Negocios y a la Economía. Ed. McGrawHill.
Newbold, P. et al. (2008). Estadística para los Negocios y la Economía. Prentice-Hall.
Triola, M.F. (2018). Estadística. Pearson.
Siegel, S. (1991). Estadística no paramétrica aplicada a las ciencias de la conducta. Ed Trillas.
Complementary
Anderson, D. R.; Sweeney, D.J. e Williams, T.A. (2001): Estadística para Administración y Economía. Vol.I. Thomson ed.
Berenson, M.L. e Levine, D.M. (1996): Estadística Básica en Administración. Conceptos y Aplicaciones. Ed. Pearson Educación / Prentice Hall.
Cao Abad, R. et at (2001). Introducción a la estadística y sus aplicaciones. 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.
Casas Sánchez, J.M. (1996): Inferencia Estadística para Economía y Administración de Empresas. Ed. Centro de Estudios Ramón Areces.
Freund, J.E.; Miller, I. e Miller, M (2000): Estadística Matemática con aplicaciones. Ed. Pearson Educación / Prentice Hall
García Barbancho, A. (1992): Estadística Teórica Básica. Probabilidad y modelos probabilísticos. Ed. Ariel.
Kazmier, L.J. (2006): Estadística aplicada a administración y economía. Ed. Mc Graw-Hill.
Gibbons, J.D., Chakraborti, S. (2003). Nonparametric statistical inference. Ed. Marcel Dekker
Levin, R.I. e Rubin, D.S. (1996): Estadística para Administradores. Ed. Pearson Educación / Prentice Hall.
Martín Pliego, F.J. e L. Ruiz-Maya. (2006): Fundamentos de Probabilidad . Ed. Thomson-Paraninfo.
Novales Cinca, A. (1998): Estadística y Econometría. Ed. McGraw Hill.
Peña, D.; Romo, j. (1997): Introducción a la Estadística para las Ciencias Sociales. Ed. McGrawHill.
Peña. D. (2014). Fundamentos de Estadística. Ed. Alianza Editorial
Ruiz-Maya, L. e F.J. Martín Pliego. (2004): Fundamentos de Inferencia Estadística . Ed. AC.
Sarabia Alegría, J.M. (2000): Curso práctico de estadística. Ed. Civitas.
Spiegel, M.R.; Schiller, J. e Alu Srinivasan, R. (2010): Probabilidad y Estadística. Ed. McGraw-Hill.
Webster A.L. (1996): Estadística aplicada a la empresa y a la economía. Ed. Irwin.
Exercises Book
Baró Llinás, J. (1987): Cálculo de probabilidades. Ed. Parramón.
Baró Llinás, J. (1989): Inferencia estadística. Ed. Parramón.
Fernández- Abascal, H., M. Guijarro, J.L. Rojo e J.A. Sanz. (1995): Ejercicios de Cálculo de Probabilidades. Ed. Ariel.
Martín Pliego, F.J., Montero Lorenzo, J.M. e Ruiz-Maya L. (1998): Problemas de probabilidad. Ed. AC.
Martín Pliego, F.J., Montero Lorenzo, J.M. e Ruiz-Maya L. (2000): Problemas de inferencia estadística. Ed. AC.
Parra Frutos, I. (2003): Estadística Empresarial con Microsoft Excel. Problemas de Inferencia Estadística. Ed. AC
BASIC AND GENERAL COMPETENCES:
CB3 That students have the ability to gather and interpret relevant data (usually within their area of study) to make judgments that include a reflection on relevant issues of a social, scientific or ethical nature
CB4 That students can transmit information, ideas, problems and solutions to both a specialized and non-specialized audience specialized.
CG2 Knowing how to elaborate and defend arguments on economic issues at a general and non-specialized level, as well as solve problems on these issues, making use of their knowledge of economic reality, theories, models and scientific methods of the economy
CG3 Know how to identify, collect and interpret relevant data on issues related to the economic field. incorporating into the preparation of judgments and proposals the pertinent considerations on their social, scientific or ethical dimension.
CG4 Know how to communicate information, ideas, problems and proposed solutions to issues of an economic nature both to a specialized and non-specialized public, making use of both verbal and written language as well as the means and techniques of representation of relationships and presentation of data used in economics
SPECIFIC COMPETENCES:
CE3 Know and understand instrumental knowledge: Basic elements of linear algebra, differential and integral calculus, optimization mathematics, descriptive statistics, probability, statistical inference, simple regression models and explanatory variables, models econometric
TRANSVERSAL COMPETENCES:
CT1 Analysis and synthesis
CT5 Knowledge of information technology
CT6 Problem solving
CT9 Autonomy in learning
CT10 Teamwork
The course will combine lectures and interactive teaching, complemented by individual and/or small group tutorials.
The subject will have a virtual classroom on the USC platform where classroom presentations and support materials for the course and preparation of the subject will be included.
LECTURE CLASSES
The sessions dedicated to lectures are aimed at introducing and explaining the basic aspects of each subject in the programme, providing the necessary additional information to allow for an adequate development of the autonomous learning process.
INTERACTIVE CLASSES AND PRACTICAL CLASSES WITH A COMPUTER
In the interactive classes, the aim is for students to learn to apply statistical techniques to the analysis of economic reality, differentiating which type of technique to use in each case, how to apply it and what conclusions are obtained from the analysis carried out. To this end, practical problems and activities will be proposed that students will have to solve individually and/or in small groups. These interactive exercises will be complemented by computer exercises, with the aim of acquiring skills in the use of spreadsheets or other statistical packages useful for data analysis.
PROGRAMMED TASKS AND TESTS
Taking the computer-based practicals as a reference, the continuous assessment activities may consist of the statistical analysis of empirical phenomena, with real databases, to be carried out throughout the four-month period, using computer tools. The aim of these tasks is for students to acquire the skills to carry out statistical analyses of empirical reality, learn to select and apply the appropriate statistical techniques for each phenomenon, to use computer tools for the analysis, to correctly interpret the results obtained, as well as to present/defend the work carried out using the lexicon of the discipline. There may also be individual or group tests to evaluate the contents developed and their comprehension.
INDIVIDUAL OR SMALL GROUP TUTORIALS
The aim of the tutorials is to provide the students with continuous advice from the teaching staff on the development of the proposed activities, many of them to be carried out autonomously by the students, as well as to attend to any doubts related to the subject. The tutoring will be primarily face-to-face but it can also be done electronically using the institutional email, the virtual classroom or the virtual platform of the USC (Teams).
At the beginning of the semester, the student may choose between a continuous assessment system and a single assessment system. The recommended evaluation system will be continuous evaluation, using instruments that make it possible to measure the continuous learning of the statistical concepts and methodologies that are the core of this subject, as well as their application to empirical reality. It will be based primarily on tasks in which the student demonstrates the level of knowledge acquired. The student who attends any test or performs any task will automatically be included in the continuous assessment system. The teaching staff of the subject recommends following this system. When choosing the assessment system, students should bear in mind that the continuous assessment system requires regular class attendance and active participation.
1ST OPPORTUNITY
A) Continuous Assessment System. Assessment instruments and their weight in the final mark:
Final exam: 70% of the final qualification (7 points)
Continuous assessment activities: 30% of the total qualification (3 points). Ppractical and computer tests, solving exercises, and/or group work.
B) Single Assessment System. Students who opt for this system will be qualified exclusively trogugh the final exam that will be score out out of 10 points.
2ND OPPORTUNITY
In the evaluation of the 2nd opportunity, students who have chosen the continuous evaluation system may remain the same or change to the single evaluation system. At the time of the exam, the student will state if he/she remains in the continuous assessment system (in which the exam scores 7 points) or if they opt for the single assessment system (in which the exam will score out of 10 points). To obtain the final grade of the subject, in the first case the continuous assessment score obtained throughout the course will be maintained, which will be added to the of the exam, while in the second case the final grade of the subject will coincide with the one obtained in the exam.
Students will not have the possibility of recovering the previous tasks, activities, and tests pending completion linked to the continuous assessment.
In order to take the exams, it will be necessary to present an official identification document (ID card, passport, etc.).
Attendance at activities will be governed by the USC's own regulations. The evaluation in cases of absence from class attendance will be made with the final test assessed on the maximum possible qualification.
According to the Permanence regulations in force in the USC for Bachelor and Master studies (art. 5.2), the mere attendance and/or participation in any of the activities subject to evaluation will mean that the student's final grade will be different from "absent".
For cases of fraudulent performance of exercises or tests, the "Regulations for the evaluation of students' academic performance and review of qualifications" shall apply.
ASSESSMENT OF COMPETENCES
Continuous Assessment: CB3, CB4, CG2, CG3, CG4, CE3, CT1, CT5, CT6, CT9, CT10
Assessment by Exam: CB3, CB4, CG4, CE3, CT1, CT5, CT6, CT9
Following the degree memory: 60 hours of face-to-face work and 90 hours of personal work by the student:
PRESENTIAL WORK
Expository teaching 31h
Interactive teaching 17h
Tutorials 9 hours
Evaluation 3h
Total hours of face-to-face work in the classroom 60 h
PERSONAL WORK OF THE STUDENT
Total hours of personal work 90 h
- To have passed the subjects Economic Statistics I and Mathematics for Economists I and II.
- Work on the subject on a daily basis in order to be able to take advantage of the classes, both to follow the subject and to ask questions in the classroom or in tutorials.
-Do not stop doing everything that is proposed to work on.
Use of the Virtual Classroom: yes
Interactive Teaching: Computer Classroom, Blackboard Classroom, Teams
Software: EXCEL, SPSS, ...
Carlos Pio Del Oro Saez
Coordinador/a- Department
- Quantitative Economy
- Area
- Quantitative Economics (USC-specific)
- carlospio.deloro [at] usc.es
- Category
- Professor: University Lecturer
Maria Luisa Chas Amil
- Department
- Quantitative Economy
- Area
- Quantitative Economics (USC-specific)
- Phone
- 881811549
- marisa.chas [at] usc.es
- Category
- Professor: University Lecturer
| Monday | |||
|---|---|---|---|
| 11:30-13:00 | Grupo /CLE_01 | Spanish, Galician | Classroom 20 |
| Tuesday | |||
| 17:00-18:30 | Grupo /CLE_02 | Spanish | Classroom 20 |
| Wednesday | |||
| 18:00-20:00 | Grupo /CLE_02 | Spanish | Classroom 20 |
| Friday | |||
| 09:30-11:30 | Grupo /CLE_01 | Galician, Spanish | Classroom 20 |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_4a | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_4b | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLE_01 | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLE_02 | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_1a | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_1b | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_2 | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_3 | Classroom 08 |
| 01.08.2025 09:00-12:00 | Grupo /CLE_01 | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLE_02 | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_1a | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_1b | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_2 | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_3 | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_4a | Classroom A |
| 01.08.2025 09:00-12:00 | Grupo /CLIL_4b | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLE_02 | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLIL_1a | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLIL_1b | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLIL_2 | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLIL_3 | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLIL_4a | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLIL_4b | Classroom A |
| 06.24.2025 12:00-15:00 | Grupo /CLE_01 | Classroom A |