ECTS credits ECTS credits: 4.5
ECTS Hours Rules/Memories Student's work ECTS: 74.25 Hours of tutorials: 2.25 Expository Class: 18 Interactive Classroom: 18 Total: 112.5
Use languages Spanish, Galician, English
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Faculty of Veterinary Science
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The main objetive is acquiring a basic training on the model of probability distribution, the basic principles of statistical inference, and their applications in the life sciences, and, particularly, in Veterinary. Related model with making decisions in the field of marketing and managemente are introduced.
Sessions in classroom
Module 1.- Descriptive statistics (8 h.)
General concept of Biostatistic and Marketing. Design of a sample. Types of data. Graphical representations. Describing of data in the field of Veterinary and Marketing. Association measures.
Module 2.- Probability and Random variables (10 h.)
Random experiment. Definition of probability and random variable. Discrete and continous random variables. The Normal distribution. Distributions asociated with the Normal distribution.
Module 3. Point estimators and confidence intervals. (8 h.)
General sample aspects. General planning of recolecting samples in Veterinary and Marketing. Point estimators. Confidence intervals for parameters of populations. Size determination of a sample. Interpretation in the field of Veterinary, Making Decisions, and Marketing.
Module 4. Hypothesis Testing (8 h.)
General proposal of Hypothesis Testing. Hypothesis Testing in Veterinary and Marketing. Hypothesis Testing for parameters of populations. Study of tables. Independence of random variables.
Sessions in computer laboratory
OR1. Formulas and Functions. (2 h.)
OR2. Descriptive statistics in the field of Veterinary and Marketing. (2 h.)
OR3. Random variables and distributions. (2 h.)
OR4 and OR5. Statistical Inference. Applications for making decisions and interpretations in the field of Veterinary and Marketing. (4 h.)
Basic bibliography
-Arriaza Gómez, A.J. e outros (2008). Estadística básica con R y R-Commander. Universidad de Cádiz.
-Cao, R. e outros (2001). Introducción a la Estadística y sus aplicaciones. Ed. Pirámide.
-González Manteiga, M.T. (2021). 400 problemas resueltos de estadística multidisciplinar. Diaz de Santos.
-Milton, J. S. (2007). Estadística para Biología y Ciencias de la Salud. McGraw-Hill.
-Pardo Fernández, J. C. (2023). Bioestatística para a enxeñaría biomédica. Servizo de Publicacións, Universidade de Vigo.
Complementary bibliography
-Daniel, W (2004). Bioestadística. Ed. Limusa.
-Elosua Oliden, P. e Etxeberria Murgiondo, J. (2012). R Commander : gestión y análisis de datos. La Muralla, D.L.
-García Pérez, A. (2010). Estadística básica con R. U.N.E.D.
-González Manteiga, M.T. (2021). 400 problemas resueltos de estadística multidisciplinar. Diaz de Santos.
-Grande, I. e Abascal E. (2009). Fundamentos y técnicas de investigación comercial. ESIC.
-Hines, W. W. e Montgomery, D. C. (1997). Probabilidad y Estadística para Ingeniería y Administración. CECSA.
-Kinnear, T.C. e Taylor, J.R. (1998). Información de mercados. Un enfoque aplicado. Mc Graw Hill.
-Luque, T. (2000). Técnicas de análisis de datos en investigación de mercados. Ed. Pirámide.
-Martín, A. e Luna, J. (2004). Bioestadística para Ciencias de la Salud. Ed. Norma.
-Miguel Álvarez, J.A. et al (2022): Probabilidad y Estadística con R Commander. Prensas de la Universidad de Zaragoza.
-Mirás Calvo, M.A.; Sánchez Rodríguez, E (2018). Técnicas estadísticas con hoja de cálculo y R. Azar y variabilidad en las ciencias naturales. Servizo de Publicacións da Universidade de Vigo.
-Norman, G. e Streiner, D. (2005). Bioestadística. Ed. Mosby.
-Samuels, M. L.; Witmer, J. A. e Schaffner, A. (2012) Fundamentos de Estadística para las Ciencias de la Vida. Pearson.
-Sarabia Alegría, J.M; Prieto Mendoza, F. e Jordá Gil, V (2018). Prácticas de estadística con R. Pirámide
-Vargas Sabadías, A. (1995). Estadística descriptiva e inferencial. Universidad de Castilla-La Mancha.
.General Competencies
o GVUSC01. Ability to learn and adapt.
o GVUSC02. Capability for analysis and synthesis.
o GVUSC03. General knowledge ofthe working area.
o GVUSC05. Capability to put knowledge into practice.
o GVUSC06. Capability to work both independently and as part of a team.
.Specific Competencies
.Disciplinary specific competencies (knowledge)
o CEDVUSC 13. To know the organizational, economic and management aspects in all fields of the veterinary profession.
.Specific Professional Competencies (expertise, day-one skills)
o D1VUSC 03. Perform standard laboratory tests, and interpret clinical, biological and chemical results.
o D1VUSC 15. Technical and economic advice and management of veterinary companies in the context of sustainability.
o D1VUSC 17. Perform technical reports specific to veterinary competencies.
.Specific Academic Competencies (want to do)
o CEAVUSC 06. Knowing how to find professional help and advice.
o CEAVUSC 08. Being aware of the need to keep professional skills and knowledge up-to-date through a process of lifelong learning.
.Transversal competences
o CTVUSC 01. Capacity for reasoning and argument.
o CTVUSC 03. Ability to develop and present an organized and understandable text.
o CTVUSC 05. Skill in the use of ICTs.
• 34 lectures supported by computer-based resources where the contents are exposed by means of practical exercises
• 10 lectures developed in the computer laboratory where an statistial program will be used.
• 1 tutorial session in small-size groups.
Dispensation to lectures developed in the computer laboratory is not applicable.
Day 1 Competency
1.2. Understand scientific research methods, the contribution of basic and applied research to science, and the application of the 3R principle (substitution, reduction, and refinement):
OA. Provide basic tools for inferential statistic applied to veterinary medicine. Will be taught in sessions OR4 and OR5.
1.9. Be able to critically review and evaluate literature and presentations:
OA: Critically review and evaluate literature of veterinary interest. Will be taught in sessions OR4 and OR5.
1.24. Use basic diagnostic equipment and perform an examination effectively as appropriate, in accordance with good health and safety practices and current regulations. Understand the contribution of digital tools and artificial intelligence in veterinary medicine.
OA. Perform a descriptive analysis of one and two variables in the computer lab and with the support of a statistical package. It will be taught in sessions OR2 and OR3.
Criteria / Percentage:
The assessment is made by means of:
a) Continuous evaluation during the course: 30% of the final qualification
b) Final written exam: 70% of the final qualification
The continuos evaluation: the student will make a written exam, with short questions.
The final exam: the student will make a written exam, with practical questions, based on the contents of the program.
Competence assessment on day 1
Competencies on days 1, 1.2, 1.9, and 1.24 are assessed, but they are not eliminatory. The assessment of these competencies counts 30% of the continuous assessment grade. In the same way as the other continuous assessment grades, repeating students maintain the grade obtained in previous courses.
Presential work: 45 (lectures: 30 hours, practical exercises: 4 hours, computer laboratory: 10 hours and tutorial session :1 hour)
Dispensation to lectures developed in the computer laboratory is not applicable.
Autonomous work: 67,5 (study: 25, individual works: 12, and resolution of proposed exercises: 27,5 hours, examinations: 3 hours)
Total hours of the student: 112,5 hours
Regular attendance to lectures, practical, and tutorial sessions.
Diary study of the subject.
Trying resolution of the proposed exercises.
Make use of the tutorial sessions to solve doubts.
Jose Maria Alonso Meijide
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- josemaria.alonso [at] usc.es
- Category
- Professor: University Professor
Luis Alberto Ramil Novo
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- l.ramil [at] usc.es
- Category
- Professor: University Lecturer
| Tuesday | |||
|---|---|---|---|
| 13:00-14:00 | Grupo /TI-ECTS01 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS04 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS07 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS10 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS13 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS02 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS05 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS08 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS11 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS03 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS06 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS09 | Spanish | Classroom 1 |
| 13:00-14:00 | Grupo /TI-ECTS12 | Spanish | Classroom 1 |
| Wednesday | |||
| 09:00-10:00 | Grupo /CLE_01 | Spanish | Classroom 1 |
| Thursday | |||
| 09:00-10:00 | Grupo /CLE_01 | Spanish | Classroom 1 |
| Friday | |||
| 09:00-10:00 | Grupo /CLE_01 | Spanish | Classroom 1 |
| 12.18.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 1 |
| 12.18.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 2 |
| 12.18.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 3 |
| 06.17.2026 09:00-11:00 | Grupo /CLE_01 | Classroom 1 |
| 06.17.2026 09:00-11:00 | Grupo /CLE_01 | Classroom 2 |