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 main objective of this course is to acquaint students with the basic concepts and techniques of Descriptive Statistics, Probability, and Statistical Inference.
In addition, students are intended to understand the need and usefulness of statistical methodology in Health Sciences research, particularly in Medicine, becoming aware of the scope and limitations of such methodologies.
1. Descriptive statistics, graphical representations.
2. Hypothesis tests. Comparison of two samples. Independent samples, paired samples. Nonparametric methods.
3. Analysis of Variance (ANOVA). Single factor model, multiple comparisons. Model with several factors, interaction. Models with repeated measurements. Nonparametric methods.
4. Regression models. Regression tests. Logistic regression. Multiple regression, interaction.
5. Contingency tables. Tests for the different types of tables.
- Rosner B. (1995) "Foundamentals of Bioestatistics". Wadsworth Publishing Company. Dubxury Press.
- Altman D.G. (1999) "Practical Statistics for Medical Research". Ed. Chapman & Hall.
- Martín Andrés A., Luna del Castillo J. (1994) "Bioestadística para las Ciencias de la Salud". 4ª ed. Ediciones Norma S.A.
- Kleinbaum D.G., Kupper L.L. and Muller K.E. (1988) "Applied Regression Analysis and other Multivariable Methods". PWS-KENT Publishing Company. Boston
- Hosmer D.W. and Lemeshow S. (1989) "Applied Logistic Regression" J.Wiley & Sons.
-Elisa T. Lee (1992) "Statistical Methods for Survival Data Analysis". Wiley Interscience.
Within this course, the aim will be to help students achieve the skills included in the report of the Master's degree in Biomedical Research:
1. Acquire knowledge about different methods of numerical presentation and graphical representation of the data collected in a study.
2. Make comparisons between data from different study groups. Control of the sample sizes required for conclusive results.
3. Analyze the relationships between the different variables observed in the experiment.
Lectures (9 hours): in the expository teaching sessions, the teaching staff will explain theoretical and practical concepts of the contents, supported by multimedia presentations. Some standard problems will also be solved, so that students can work on the exercise reports that will be provided. With regard to the material for monitoring the course in addition to recommended bibliography, students will have complementary teaching material available through the course's Virtual Campus.
Interactive teaching (12 hours): interactive teaching is distributed in seminars for solving classroom exercises and computer practices. In these sessions, students will be introduced to the use of the R package for statistical data analysis. They will also have to work and solve practical cases. To follow up the computer practice sessions, students will be provided with practice scripts.
Tutorials: the tutorials are intended to follow up the students' learning. They will be carried out both in person and through e-mail or MS Teams. In these sessions we will work mainly on those competences related to critical reasoning and communication skills.
The methodology will be adapted as follows to the different possible scenarios:
Expository and interactive teaching will be face-to-face and will be complemented with the course's Virtual Campus in which students will find bibliographic materials, case studies, explanatory videos, etc. Through the Virtual Campus, students will also be able to carry out tests and tasks for continuous assessment, as described in the corresponding section.
Evaluation and learning system
Continuous evaluation (25%): continuous evaluation will be based on participation in two types of tasks: those associated with interactive seminar sessions (individual or group problem solving) and those associated with interactive laboratory sessions (resolution of practical exercises with R, the statistical suite). These activities can be carried out both in the classroom and in the field.
Final exam (75%): the final exam will consist of several theoretical and practical questions and case studies, within which the interpretation of results obtained with the statistical package used in interactive teaching may be included.
The weight of the continuous evaluation in the recovery opportunity will be the same as in the ordinary call of the semester.
The course's final grade will not be lower than that of the final exam or that obtained by weighting 25% of the continuous assessment and 75% of the final exam grade.
Evaluation will be carried out by combining a continuous formative evaluation with a final exam under previously indicated conditions.
It should be noted that in cases of fraudulent exercises or tests, the provisions of the "Regulations for the Evaluation of Students' Academic Performance and for the Review of Grades" will apply.
In this course, students have 21 hours of classroom teaching (9 hours of lectures and 12 hours of interactive teaching). It is considered necessary to dedicate around 51 hours of student’s work to the study (for concept review and consultation of bibliography).
Students should keep in mind that it is necessary to make practice solving problems (either from bulletins or from the recommended bibliography).
In case that sessions cannot be carried out in a face-to-face or synchronous virtual way, it is understood that these hours must be used by the student in a personal capacity for the preparation of the course.
Attendance at the exhibition and interactive sessions is essential to follow up and overcome the subject. Students must carry out all the activities recommended by the teaching staff (literature review and case studies) to successfully complete the subject.
The course material will be made available to students through the USC Virtual Campus. We intend to make this platform, together with MS Teams and e-mail, the main means of online 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 | Medicine-Classroom 9 |