ECTS credits ECTS credits: 2
ECTS Hours Rules/Memories Student's work ECTS: 34 Hours of tutorials: 2 Expository Class: 6 Interactive Classroom: 8 Total: 50
Use languages Spanish, 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 Nursing
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
- Familiarize students with the basic techniques of statistics, both descriptive and inferential, and their application in Health Sciences in general, and in Nursing in particular.
- Assess the importance of statistics as a tool to access scientific knowledge and learn to critically assess the information from research reports based on its rigor in the application of statistics and the conclusions drawn from the analyzes carried out.
- Use a statistical package that allows the analysis of data obtained in a biomedical research.
1. The Statistical Method in Health Care, Management and Care
2. Obtaining data. Sample design
3. Introduction to the statistical packages SPSS and Epidat 4.2
4. Descriptive statistics and graphic representations
5. Correlation and regression in quantitative variables
6. Typical probability distributions in Biomedicine
7. Statistical Inference. Applications in Healthcare, Management and Care
COMPUTER CLASSROOM PRACTICES
Practice 1.- Introduction to the SPSS. Descriptive statistics
Practice 2.- Regression and dependency of variables
Practice 3.- Statistical Inference
Practice 4.- Introduction to EPIDAT 4.2
The study materials for the subject will be available on the virtual campus, in which the theoretical contents, illustrative examples, exercise bulletins for the seminars and computer practice scripts will be developed.
BASIC BIBLIOGRAPHY
Crujeiras, R.M. y Faraldo, P. (2010): "Manual de estadística básica para ciencias de la salud", Unidixital
Rius Díaz, F. e Wärnerberg Wärnerberg, Julia (2014): Bioestadística. Paraninfo.
COMPLEMENTARY BIBLIOGRAPHY
Glover, T.; Mitchell, K. (2015): "An Introduction to Biostatistics using R", Waveland Press. Available at:
https://waveland.com/Glover-Mitchell/r-guide.pdf [Accessed May 12, 2024]
Martínez González, M.A.; Sánchez Villegas, A.; Toledo Atucha , E.; Faulin Fajardo, J. (2020): "Bioestadística amigable", Elsevier.
Montanero Fernández, J.; Minuesa Abril, C. (2018): "Estadística básica para Ciencias de la salud". Available at:
http://matematicas.unex.es/~jmf/Archivos/Manual%20de%20Bioestadística.p… [Accessed May 12, 2024]
Milton J.S. (2007) Estatística para Bioloxía e Ciencias da Saúde. Interamericana- McGraw- Hill (3ª edición).
Rosner, B. (2011) “ Fundamentals of Bioestatistics”. Wadsworth Publishing Company. Duxbury Press (5ª edición).
Verzani, J. (2002): "simpleR. Using R for Introductory Statistics". Dispoñible en:
https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf [Consulta: 12 de maio de 2024]
1. Basic and General
CG02 - Ability to collect data of sanitary interest, analyze and transmit it effectively
CG03 - Ability to use the new information systems in the areas of performance of the profession
CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context
CB7 - That students know how to apply the acquired knowledge and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of study
CB8 - That students are able to integrate knowledge and face the complexity of formulating judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments.
CB9 - That students know how to communicate their conclusions and the latest knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way
CB10 - That students possess the learning skills that allow them to continue studying in a way that will have to be largely self-directed or autonomous
2. Transversals
CT01 - Ability to take actions with team spirit
CT02 - Ethical and intellectual commitment.
3. Specific
CE37 - Develop capacities to describe and synthesize the information from the data collected at various measurement scales, using descriptive statistics and graphic procedures.
CE38 - Understand the fundamentals of hypothesis testing, as well as its relationship with parameter estimation procedures using confidence intervals
CE39 - Know and know how to apply and interpret the main statistical tests for the comparison of means CE40 - Know and interpret categorical data tables and know how to apply chi-square tests.
Scenario 1 (adapted normality)
The theoretical-practical concepts of the contents will be explained in the expository teaching sessions in the classroom, supported by multimedia presentations. Regarding the material for the follow-up of the subject, students over the
Scenario 1 (adapted normality) The theoretical-practical concepts of the contents will be explained in the expository teaching sessions in the classroom, supported by multimedia presentations. Regarding the material for the follow-up of the subject, students over the recommended bibliography will have complementary teaching material. The interactive laboratory classes will be held in the computer room where students will be introduced to the management of the SPSS and EPIDAT 4.2 for statistical data analysis. This session will also work on clinical cases shared with other course subjects. For the follow-up of the practical classes, the script of the same will be provided to the students.
The tutorials are aimed at monitoring student learning.
Scenarios 2 (distancing) and 3 (closing of the facilities)
The above is modified for when it is not possible to teach face-to-face, it will be replaced by synchronous teaching through the TEAMS platform complemented with guidance and materials in the virtual course
A final qualification will be or result of a theoretical-practical final test, which will correspond to 50% of the final grade, and the evaluation of the interactive activities, which will be the remaining 50% in the final qualification.
The exam will be face-to-face or telematic depending on the setting.
For cases of fraudulent performance of exercises or tests, the provisions of the "Regulations for evaluating student academic performance and reviewing grades" will apply.
The study time and individual work depends heavily on the previous statistical knowledge of the student. For each lecture hour, about 1.5 hours is considered necessary for the revision of concepts and bibliography consulting. For the interactive part, one hour of self-study for each session hour would be enough for revising the class work.
Attending the lectures and interactive sessions is crucial for following the course and passing the assessment. The student must work on all the activities recommended by the professor (solving exercise, revising bibliography and practical exercises) in order to pass the course.
The students will have the materials of the course in the Virtual Campus (Moodle). In these materials are the contents (theoretical and practical) of the subject.
Pedro Faraldo Roca
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813216
- pedro.faraldo [at] usc.es
- Category
- Professor: University Lecturer
| Tuesday | |||
|---|---|---|---|
| 16:00-18:00 | Grupo /CLE_01 | Spanish | Computer 5.01 |
| 18:00-20:30 | Grupo /CLIL_01 | Spanish | Computer 5.01 |
| 01.24.2025 16:00-18:00 | Grupo /CLE_01 | Classroom 2.03 |
| 07.11.2025 16:00-18:00 | Grupo /CLE_01 | Classroom 2.03 |