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.)
-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.
-Elosua Oliden, P. y Etxeberria Murgiondo, J. (2012). R Commander : gestión y análisis de datos. La Muralla, D.L.
-Delgado de la Torre, R. (2007). Probabilidad y Estadística para Ciencias e Ingenierías. Delta Publicaciones.
-García Pérez, A. (2010). Estadística básica con R. U.N.E.D.
-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.
-Milton, J. S. (2004). Estadística para Biología y Ciencias de la Salud. McGraw-Hill.
-Navidi, W. (2006). Estadística para Ingenieros y Científicos. McGraw-Hill.
-Norman, G. y Streiner, D. (2005). Bioestadística. Ed. Mosby.
-Novo Sanjurjo, V. (1993). Problemas de Cálculo de Probabilidades y Estadística. U.N.E.D.
-Parra Frutos, I. (2003). Estadística Empresarial con Microsoft Excel. Problemas de Inferencia Estadística. Ed. AC.
-Peña Sánchez de Rivera, D. (2001). Fundamentos de Estadística. Alianza Editorial.
-Peña Sánchez de Rivera, D. (2002). Regresión y diseño de experimentos. Alianza Ed.
-Samuels, M. L.; Witmer, J. A. y Schaffner, A. (2012) Fundamentos de Estadística para las Ciencias de la Vida. Pearson.
-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.
The teaching methodology scenarios will be adapted to the university scenarios (1, 2, and 3).
These scenarios will determine different attendance conditions to the three type of sessions.
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.
The assessment scenarios will be adapted to the university scenarios (1, 2, and 3).
Presential work in class and laboratory: 45 hours
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.
ADAPTATION OF TEACHING ACTIVITIES TO THE SCENARIOS
Below are the aspects to take into account for adaptation to the scenario adopted by the university.
- The teaching and assessment scenarios will be adapted to the university scenarios.
- These scenarios will determine different attendance conditions to the three type of sessions.
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 Lecturer
| Thursday | |||
|---|---|---|---|
| 11:00-12:00 | Grupo /CLE_01 | Spanish | Auditorium |
| Friday | |||
| 11:00-12:00 | Grupo /CLE_01 | Spanish | Auditorium |
| 01.15.2021 09:00-11:00 | Grupo /CLE_01 | Classroom 1 |
| 01.15.2021 09:00-11:00 | Grupo /CLE_01 | Classroom 2 |
| 01.15.2021 09:00-11:00 | Grupo /CLE_01 | Classroom 3 |
| 01.15.2021 15:00-17:00 | Grupo /CLE_01 | Computer room 1 |
| 07.08.2021 09:00-11:00 | Grupo /CLE_01 | Classroom 1 |
| 07.08.2021 09:00-11:00 | Grupo /CLE_01 | Classroom 2 |
| 07.08.2021 15:00-17:00 | Grupo /CLE_01 | Computer room 1 |