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 G贸mez, A.J. y otros (2008). Estad铆stica b谩sica con R y R-Commander. Universidad de C谩diz.
-Cao, R. y otros (2001). Introducci贸n a la Estad铆stica y sus aplicaciones. Ed. Pir谩mide.
-Daniel, W (2004). Bioestad铆stica. Ed. Limusa.
-Gonz谩lez Manteiga, M.T. (2021). 400 problemas resueltos de estad铆stica multidisciplinar. Diaz de Santos.
-Milton, J. S. (2004). Estad铆stica para Biolog铆a y Ciencias de la Salud. McGraw-Hill.
Complementary bibliography
-Elosua Oliden, P. y Etxeberria Murgiondo, J. (2012). R Commander : gesti贸n y an谩lisis de datos. La Muralla, D.L.
-Garc铆a P茅rez, A. (2010). Estad铆stica b谩sica con R. U.N.E.D.
-Gonz谩lez Manteiga, M.T. (2021). 400 problemas resueltos de estad铆stica multidisciplinar. Diaz de Santos.
-Grande, I. y Abascal E. (2009). Fundamentos y t茅cnicas de investigaci贸n comercial. ESIC.
-Hines, W. W. y Montgomery, D. C. (1997). Probabilidad y Estad铆stica para Ingenier铆a y Administraci贸n. CECSA.
-Kinnear, T.C. y Taylor, J.R. (1998). Informaci贸n de mercados. Un enfoque aplicado. Mc Graw Hill.
-Luce帽o, A. y Gonz谩lez, F. J. (2004). M茅todos estad铆sticos para medir, describir y controlar la variabilidad. Universidad de Cantabria.
-Luque, T. (2000). T茅cnicas de an谩lisis de datos en investigaci贸n de mercados. Ed. Pir谩mide.
-Mart铆n, A. y Luna, J. (2004). Bioestad铆stica para Ciencias de la Salud. Ed. Norma.
-Miguel 脕lvarez, J.A. et al (2022): Probabilidad y Estad铆stica con R Commander. Prensas de la Universidad de Zaragoza.
-Mir谩s Calvo, M.A.; S谩nchez Rodr铆guez, E (2018). T茅cnicas estad铆sticas con hoja de c谩lculo y R. Azar y variabilidad en las ciencias naturales. Servizo de Publicaci贸ns da Universidade de Vigo.
-Norman, G. y Streiner, D. (2005). Bioestad铆stica. Ed. Mosby.
-Novo Sanjurjo, V. (1993). Problemas de C谩lculo de Probabilidades y Estad铆stica. U.N.E.D.
-Samuels, M. L.; Witmer, J. A. y Schaffner, A. (2012) Fundamentos de Estad铆stica para las Ciencias de la Vida. Pearson.
-Sarabia Alegr铆a, J.M; Prieto Mendoza, F.; Jord谩 Gil, V (2018). Pr谩cticas de estad铆stica con R. Pir谩mide
-Vargas Sabad铆as, A. (1995). Estad铆stica descriptiva e inferencial. Universidad de Castilla-La Mancha.
.General Competencies
o GV奇趣腾讯分分彩01. Ability to learn and adapt.
o GV奇趣腾讯分分彩02. Capability for analysis and synthesis.
o GV奇趣腾讯分分彩03. General knowledge ofthe working area.
o GV奇趣腾讯分分彩05. Capability to put knowledge into practice.
o GV奇趣腾讯分分彩06. Capability to work both independently and as part of a team.
.Specific Competencies
.Disciplinary specific competencies (knowledge)
o CEDV奇趣腾讯分分彩 13. To know the organizational, economic and management aspects in all fields of the veterinary profession.
.Specific Professional Competencies (expertise, day-one skills)
o D1V奇趣腾讯分分彩 03. Perform standard laboratory tests, and interpret clinical, biological and chemical results.
o D1V奇趣腾讯分分彩 15. Technical and economic advice and management of veterinary companies in the context of sustainability.
o D1V奇趣腾讯分分彩 17. Perform technical reports specific to veterinary competencies.
.Specific Academic Competencies (want to do)
o CEAV奇趣腾讯分分彩 06. Knowing how to find professional help and advice.
o CEAV奇趣腾讯分分彩 08. Being aware of the need to keep professional skills and knowledge up-to-date through a process of lifelong learning.
.Transversal competences
o CTV奇趣腾讯分分彩 01. Capacity for reasoning and argument.
o CTV奇趣腾讯分分彩 03. Ability to develop and present an organized and understandable text.
o CTV奇趣腾讯分分彩 05. Skill in the use of ICTs.
鈥 34 lectures supported by computer-based resources where the contents are exposed by means of practical exercises
鈥 10 lectures developed in the computer laboratory where an statistial program will be used.
鈥 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-ECTS11 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS09 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS08 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS04 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS03 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS01 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS10 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS12 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS06 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS05 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS02 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS07 | Spanish | Classroom 3 |
| 13:00-14:00 | Grupo /TI-ECTS13 | 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 |