ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Student's work ECTS: 51 Hours of tutorials: 3 Expository Class: 9 Interactive Classroom: 12 Total: 75
Use languages Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Faculty of Medicine and Dentistry
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The general objective of this course is for the student to fluently handle concepts and techniques of Descriptive Statistics and Statistical Inference. In addition, it is intended that students understand the need and usefulness of statistical methodology in biomedical research, becoming aware of its scope and limitations.
The specific objectives of the course are:
- Acquire knowledge about different methods of numerical presentation and graphic representation of the data collected in a study.
- Make comparisons between data from different study groups.
- Analyze the relationships between the different variables observed in the experiment.
Topic 0.- Introduction to R.
Topic 1.- Descriptive Statistics.
Topic 2.- Estimation and hypotheses testing.
Topic 3.- Analysis of Variance (ANOVA).
Topic 4.- Regression models.
All the theoretical content will be accompanied by the corresponding resolution of examples and exercises using the statistical package R.
BASIC
- Altman D.G. (1999) "Practical Statistics for Medical Research". Ed. Chapman & Hall.
- Borrajo, M. I. et al. (2020): Estatística Descritiva. Colección Esenciais USC.
https://www.usc.gal/libros/gl/categorias/948-estatistica-descritiva-334…
- Borrajo, M. I. et al. (2021): Fundamentos da Teoría da Probabilidade. Colección Esenciais USC.
https://www.usc.gal/libros/gl/categorias/1025-fundamentos-da-teoria-da-…
- Borrajo, M. I. et al. (2021): O programa estatístico R. Colección Esenciais USC.
https://www.usc.gal/libros/gl/categorias/1024-o-programa-estatistico-r-…
- Borrajo, M. I. et al. (2023): Inferencia Estatística Paramétrica I. Colección Esenciais USC.
https://www.usc.gal/libros/gl/categorias/1183-inferencia-estatistica-pa…
- Borrajo, M. I. et al. (2023): Inferencia Estatística Paramétrica II. Colección Esenciais USC.
https://www.usc.gal/libros/gl/categorias/1182-inferencia-estatistica-pa…
- Faraway, J.J. (2015). Linear models with R (2nd edition). Chapman and Hall.
- Kleinbaum D.G., Kupper L.L. and Muller K.E. (1988) "Applied Regression Analysis and Other Multivariable Methods". PWS-KENT Publishing Company. Boston.
- Milton, J.S. (2007): “Estadística para Biología y Ciencias de la Salud”, McGraw-Hill-Interamericana.
- Rosner B. (1995) "Fundamentals of Biostatistics". Wadsworth Publishing Company. Duxbury Press.
- Verzani, J. (2005). Using R for Introductory Statistics. Chapman and Hall.
COMPLEMENTARY
- Cao, R. et al. (1998). Estadística básica aplicada. Tórculo Edicións.
- Martin Andrés A., Luna del Castillo J. (1994) "Bioestadística para las Ciencias de la Salud". 4ª ed. Ediciones Norma S.A.
- Faraway, J.J. (2006). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman and Hall.
- Hosmer D.W. and Lemeshow S. (1989). "Applied Logistic Regression" J.Wiley & Sons.
Upon completion of this course, students are expected to work on the skills listed in the Master's in Biomedical Research at the Universidade de Santiago de Compostela. Thus, students must acquire the following basic, general and transversal skills:
CG1 - Know how to apply the appropriate techniques to solve a specific problem in Biomedicine, and be able to carry out a research project on the subject under supervision, not only in the subjects covered by the subjects, but in broader or even multidisciplinary contexts.
CG2 - Being able to make judgments about hypotheses, experimental proposals or experiments already carried out in the field of biomedical research, both in terms of scientific validity and in the ethical and social aspects of the trial.
CG3 - Being able to work as a team in a multidisciplinary environment to achieve common goals from different perspectives.
CG4 - Being able to communicate their proposals, experiments, results, conclusions and criticisms to specialized and non-specialized audiences.
CG5 - Possess the necessary learning skills to keep up-to-date in the field of biomedical research and its techniques independently.
In addition, the specific competence will also be worked on:
CE5 - Be able to design experiences in the field of Biomedicine, applying the appropriate techniques to answer the question posed.
Expository teaching (9 hours): in the expository teaching sessions, the teaching staff will explain the theoretical and practical concepts of the contents, with the help of presentations. Regarding the material for the follow-up of the subject, in addition to the recommended bibliography, students will have complementary didactic material through the Virtual Campus of the subject.
Interactive teaching (12 hours): Interactive teaching is distributed in case-solving seminars in the classroom with the computer. In these sessions, students will be introduced to the handling of the R package for statistical data analysis. They will also work on the practical cases that they will have to solve.
Tutoring: the purpose of the tutorial is to monitor the student's learning. They will be carried out both in person and via email or MS Teams. Fundamentally those skills related to critical reasoning and communication skills will be worked on.
The final grade for the course is structured as follows in the regular exam session.
Continuous assessment (75%):
- Preparation of an individual or group statistical report: 40%
- Peer review of another report: 25%
- Practical test on the use of the R statistical software: 10%
Final exam (25%): oral presentation of the statistical report to classmates, including a round of questions and discussion. This activity will be assessed based on both the content presented and the student's communication and oral defense skills.
In the resit session, the assessment is as follows:
- Preparation of an individual statistical report: 40%
- Review of another report: 25%
- Practical test on the use of the R statistical software: 10%
- Oral presentation of the statistical report to the teaching staff, including a round of questions and discussion. This activity will be assessed based on both the content presented and the student's communication and oral defense skills: 25%
The format of the final exam—oral presentation—is defined in this guide in accordance with the requirements of the degree program and will be applied in the regular session and, where applicable, in the resit session.
No minimum attendance at face-to-face sessions is required to qualify for continuous assessment or to sit the final exam, in accordance with Article 1 of the USC Regulations on class attendance in official undergraduate and master's programs.
In cases of fraudulent completion of exercises or tests, the provisions of the Regulations on the academic performance evaluation of students and the review of grades shall apply.
In this subject, students have 21 hours of face-to-face teaching (9 hours of expository teaching and 12 hours of interactive teaching). For expository teaching, it is considered necessary to dedicate about 49 hours of personal work by the student to the study (review of concepts and consultation of bibliography). Students must take into account that it is also necessary to practice solving problems and practical cases independently.
Regular attendance at face-to-face sessions is recommended, as it facilitates following the course, understanding the content, and acquiring the expected competencies. Students are expected to complete all activities recommended by the teaching staff (review of bibliography and practical cases) in order to successfully pass the course.
The course material will be made available to students through the USC Virtual Campus. It is intended that this platform be the main means of communication with students.
Maria Isabel Borrajo Garcia
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaisabel.borrajo [at] usc.es
- Category
- Professor: Temporary PhD professor
| Friday | |||
|---|---|---|---|
| 08:30-11:30 | Grupo /CLE_01 | Galician | Aulario-Classroom 10 |