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
Lesson 1. Formulas and Functions. (2 h.)
Lesson 2. Descriptive statistics in the field of Veterinary and Marketing. (2 h.)
Lesson 3. Random variables and distributions. (2 h.)
Lessons 4 and 5. Statistical Inference. Applications for making decisions and interpretations in the field of Veterinary and Marketing. (4 h.)
Basic bibliography
-Arriaza Gómez, A.J. y otros (2008). Estadística básica con R y R-Commander. Universidad de Cádiz.
-Cao, R. y otros (2001). Introducción a la Estadística y sus aplicaciones. Ed. Pirámide.
-Daniel, W (2004). Bioestadística. Ed. Limusa.
-González Manteiga, M.T. (2021). 400 problemas resueltos de estadística multidisciplinar. Diaz de Santos.
-Milton, J. S. (2004). Estadística para Biología y Ciencias de la Salud. McGraw-Hill.
Complementary bibliography
-Elosua Oliden, P. y 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. y Abascal E. (2009). Fundamentos y técnicas de investigación comercial. ESIC.
-Hines, W. W. y Montgomery, D. C. (1997). Probabilidad y Estadística para Ingeniería y Administración. CECSA.
-Kinnear, T.C. y Taylor, J.R. (1998). Información de mercados. Un enfoque aplicado. Mc Graw Hill.
-Luceño, A. y González, F. J. (2004). Métodos estadísticos para medir, describir y controlar la variabilidad. Universidad de Cantabria.
-Luque, T. (2000). Técnicas de análisis de datos en investigación de mercados. Ed. Pirámide.
-Martín, A. y 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. y Streiner, D. (2005). Bioestadística. Ed. Mosby.
-Novo Sanjurjo, V. (1993). Problemas de Cálculo de Probabilidades y Estadística. U.N.E.D.
-Samuels, M. L.; Witmer, J. A. y Schaffner, A. (2012) Fundamentos de Estadística para las Ciencias de la Vida. Pearson.
-Sarabia Alegría, J.M; Prieto Mendoza, F.; 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.
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
C1.24 Use basic diagnostic equipment and perform an examination effectively as appropriate, in accordance with good safety and health practices and current regulations. Understand the contribution of digital tools and artificial intelligence in veterinary medicine.
Objective
To perform in the computer classroom and with the support of a statistical package a descriptive analysis of one and two variables (C1.24) (Practical).
Task
T1. Students will perform a basic statistical analysis of data of one and two variables with the support of a computer program (O1). (O1)
Technique of evaluation of the competence C1.24
It will be carried out by means of a questionnaire with short questions, in the last minutes of the third or fourth practice session. The evaluation of this objective will be considered passed if a minimum of 5 points out of a maximum of 10 is obtained in the questionnaire.For those who pass positively the evaluation of this objective, the grade of this test will count as 5% of the total grade, and will be included in the continuous evaluation section.Those who do not pass this test will not be able to pass the subject.
Repeating students: Repeating students will pass this part of the competency assessment on day 1.
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-ECTS13 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS02 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS05 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS08 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS11 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS03 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS06 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS09 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS12 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS01 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS04 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS07 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS10 | Spanish | Classroom 3 |
| Wednesday | |||
| 09:00-10:00 | Grupo /CLE_01 | Spanish | Classroom 3 |
| Thursday | |||
| 09:00-10:00 | Grupo /CLE_01 | Spanish | Classroom 3 |
| Friday | |||
| 09:00-10:00 | Grupo /CLE_01 | Spanish | Classroom 3 |
| 01.22.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 1 |
| 01.22.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 2 |
| 01.22.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 3 |
| 07.02.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 1 |
| 07.02.2025 09:00-11:00 | Grupo /CLE_01 | Classroom 2 |