ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 3 Expository Class: 36 Interactive Classroom: 21 Total: 60
Use languages Spanish, Galician
Type: Ordinary Degree Subject 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 familiarize students with the basic concepts and techniques of Descriptive Statistics, Probability Theory, and Statistical Inference. The goal is for students to understand the need for and usefulness of statistical methodology in research in the Health Sciences, particularly in the field of Medicine.
The specific objectives of this course are detailed below:
- To be able to distinguish between the objectives of a statistical analysis: descriptive or inferential.
- To be able to distinguish between a statistical population and a sample thereof.
- To synthesize and describe a large amount of data by selecting statistics appropriate to the type of variables and analyzing the relationships between them.
- To understand the probabilistic basis of Statistical Inference, as well as the general principles of the most common probabilistic models.
- To be able to estimate unknown parameters of a population from a sample.
- To understand the principles and applications of hypothesis testing.
- Know how to compare two populations based on their characteristic and unknown parameters.
- Know how to formulate real-life problems in statistical terms and apply Statistical Inference to their resolution.
- Be able to use statistical software packages.
- Understand the necessity and usefulness of Statistics as a tool in their professional practice, being aware of the degree of subjectivity and the risk of decisions based on statistical results.
1. Descriptive Statistics.
Definition and objectives of statistics. Statistics in medical research. Study design, population, and sample. Types of statistical variables. Summary of the information contained in a sample:
frequency tables and graphical representations. Measures of centrality, position, dispersion, and shape.
2. Calculation of Probabilities.
Randomized experiment. Event and sample space. Operations with events. Axiomatic definition of probability. Conditional probability. Independence of events. Product rule. Theorem of total probabilities. Bayes' rule. Prevalence and incidence of a disease. Diagnostic tests:
sensitivity, specificity, and predictive values.
3. Discrete random variables.
Concept of one-dimensional random variables. Discrete random variables. Probability mass, distribution function, and survival function. Characteristic measures: expected value and variance. Binomial distribution. Poisson distribution.
4. Continuous random variables.
Continuous random variable. Density function, distribution function, and survival function.
Characteristic measures: expected value and variance. The normal distribution. Cutoff points for binormal diagnostic tests. Central Limit Theorem. Approximation of the binomial distribution by the normal distribution. Distributions associated with the normal distribution: Chi-square, Student's t-test.
5. Point and interval estimates.
Objectives of Statistical Inference. Concepts of parameters and statistics. Sampling distributions of statistics of interest. Point estimates of the mean, variance, and proportion. Bias and variance.
Confidence intervals for the mean (in normal populations) and for the proportion. Determining sample size.
6. Introduction to hypothesis testing.
Basic concepts: Null and alternative hypotheses; one-sided and two-sided tests; acceptance and rejection zones; Type I error and significance level; Type II error and power; p-value.
Tests on the mean (in normal populations) and on the proportion. Comparison tests of means in normal populations (for two independent or paired samples) and of proportions.
7. Tests for categorical variables.
Contingency tables. Observed and expected frequencies. Chi-square test of independence. Yates correction. 2x2 contingency tables in medicine. Measures of association: Relative risk and odds ratio.
8. Simple linear regression model.
Scatter plot. Covariance and linear correlation coefficient. Method of least squares.
Inference about parameters. Variability decomposition. The F test. Coefficient of determination. Model diagnosis. Prediction.
- Alonso Pena, M., Bolón Rodríguez, D., Ameijeiras Alonso, J., Saavedra Nieves, A. and Saavedra Nieves, P. (2024). Manual de R para prácticas deBioestadística. Servizo de Publicacións da Universidade de Santiago de Compostela. DOI: https://dx.doi.org/10.15304/9788419679536.
- Álvarez Cáceres, R. (2007) “Estadística Aplicada a las Ciencias de la Salud”. Editorial Diaz de Santos.
- Daniel, W.W. (2006) “Bioestadística. Base para el análisis de las ciencias de la salud”. (2ª ed). Editorial LIMUSA. Wiley.
- Douglas G. A. (1997) “Practical Statistics for Medical Research”. Ed. Chapman & Hall.
- Martín Andrés, A. and Luna del Castillo, J. (1994) “Bioestadística para las ciencias de la salud”. (4ª ed). Ediciones Norma.
- Martín Andrés, A. and Luna del Castillo, J. (1995) “50 +/- 10 horas de Bioestadística”. Ediciones Norma.
- Martínez González, M.A; Sánchez, A. and Faulin, J. (2006). “Bioestadística amigable”. 2ª ed. Editorial Diaz de Santos.
- Milton, J.S. (1994) “Estadística para biología y ciencias de la salud”. (2ª ed). Ed. Interamericana, McGraw-Hill.
- Paradis, E. (2003). R para principiantes. R Cran. Disponible en https:/ cran.r- project.org/doc/contrib/rdebuts_es.pdf
- Quesada, V. and otros (1982) “Curso de ejercicios de estadística”. (2ª ed). Editorial Alambra.
- Rosner, B. (2000) “Fundamentals of Biostatistics”. (5ª ed). Wadsworth Publishing Company. Duxbury Press.
- Venables, W.N., Smith, D.M. and the R Core Team (2020). An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics (Version 3.6.3). Disponible en https:/ cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
- Verzani, J. (2005). Using R for Introductory Statistics. Chapman and Hall.
Knowledge:
Con53. Understand, critically assess, and know how to use clinical and biomedical information technologies and sources to obtain, organize, interpret, and communicate clinical, scientific, and healthcare information.
Con54. Understand the basic concepts of biostatistics and their application to medical sciences.
Con55. Be able to design and conduct simple statistical studies using computer programs and interpret the results.
Con56. Understand and interpret statistical data in the medical literature.
Con59. Independently operate a personal computer and the most common computer applications in the field of biomedicine.
Con62. Critically understand and interpret scientific texts.
Competencies:
Comp01. Analytical and synthetic skills.
Comp05. Basic computer skills.
Comp06. Information management skills (ability to search for and analyze information from various sources).
Comp07. Problem-solving.
Comp08. Decision-making.
Comp09. Critical and self-critical skills.
Comp13. Ability to communicate with experts in other fields.
Comp17. Ability to apply knowledge in practice.
Comp18. Research skills.
Comp19. Ability to learn.
Skills or abilities:
H/D28. Obtain and use epidemiological data and assess trends and risks for health-related decision-making.
H/D31. Know, critically assess, and know how to use sources of clinical and biomedical information to obtain, organize, interpret, and communicate scientific and healthcare information.
H/D32. Know how to use information and communication technologies in clinical, therapeutic, preventive, and research activities.
H/D33. Maintain and use records with patient information for subsequent analysis, preserving data confidentiality.
H/D34. Maintain, in professional activity, a critical and creative perspective, with constructive skepticism and a research-oriented approach.
H/D36. Be able to formulate hypotheses, collect, and critically assess information to resolve problems, following the scientific method.
H/D37. Acquire basic training for research activities.
Lecture and interactive teaching will be in-person. Students will find notes and problem sets on the Virtual Campus for the course. Additionally, through the Virtual Campus, students will be able to take tests and submit continuous assessment assignments, as described in the corresponding section.
Lecture (36 hours): In the lecture sessions, the instructor will explain the theoretical and practical concepts of the content, supported by multimedia presentations. Some standard problems will also be solved so that students can work on the exercise sets provided. Regarding material for following the course, in addition to the recommended bibliography, students will have access to supplementary teaching materials on the Virtual Campus.
Interactive teaching (21 hours): Interactive teaching is divided into exercise-solving seminars and computer practice sessions. In these sessions, students will be introduced to the use of the R package for statistical data analysis by working on practical cases.
Tutorials (3 hours): Tutorials are designed to monitor student learning. These sessions will primarily focus on skills related to critical reasoning and communication skills.
Continuous Assessment (30%): Two tests will be administered, addressing questions and problems similar to those solved in the seminar sessions, each with a weight of 5%. A third test, with a weight of 20%, will be assigned to solve practical cases using the statistical program R. These activities may be conducted in person or online.
Final Exam (70%): The final exam will consist of several theoretical and practical questions and problems related to the subject matter.
The weight of continuous assessment during the retake opportunity will be the same as during the regular semester exam session.
Please note that, in cases of fraudulent completion of exercises or tests, the provisions of the "Regulations on the Evaluation of Student Academic Performance and the Review of Grades" will apply.
In this subject, students have 60 hours of in-person instruction (36 hours of lectures, 21 hours of interactive instruction, and 3 hours of tutorials).
Approximately 90 hours of individual student work are required. In addition, students must practice solving problems from the handouts or recommended bibliography.
It is recommended that students attend the lectures and interactive sessions, as well as the proposed activities, as essential tools for mastering the course content.
To successfully complete the course, it is also advisable to follow the proposed work plans. It is also recommended that students practice using the statistical package R to explore the possibilities of the various techniques explained throughout the course.
The course materials will be made available to students through the USC Virtual Campus. We intend for this platform to be the primary means of communication with students, reinforced with MS Teams and email.
Paula Saavedra Nieves
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- paula.saavedra [at] usc.es
- Category
- Professor: University Lecturer
Alejandro Saavedra Nieves
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- alejandro.saavedra.nieves [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Maria Alonso Pena
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaalonso.pena [at] usc.es
- Category
- Professor: Intern Assistant LOSU
Iria Rodríguez Acevedo
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- iriarodriguez.acevedo [at] usc.es
- Category
- Xunta Pre-doctoral Contract
| Monday | |||
|---|---|---|---|
| 09:30-10:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 12:30-13:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 16:30-17:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 19:30-20:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| Tuesday | |||
| 09:30-10:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 12:30-13:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 16:30-17:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 18:30-19:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 19:30-20:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| Wednesday | |||
| 09:30-10:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 12:30-13:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 16:30-17:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 19:30-20:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| Thursday | |||
| 09:30-10:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 12:30-13:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 16:30-17:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 18:30-19:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 19:30-20:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| Friday | |||
| 09:30-10:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 12:30-13:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 16:30-17:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Galician | Medicine-Classroom 4 |
| 19:30-20:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 2 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 3 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 4 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 5 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 6 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 7 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicine-Classroom 8 |
| 12.15.2025 12:00-14:30 | Grupo /CLE_01 | Medicina-Aula 10 |
| 06.12.2026 09:30-12:00 | Grupo /CLE_01 | Medicine-Classroom 3 |
| 06.12.2026 09:30-12:00 | Grupo /CLE_01 | Medicine-Classroom 4 |
| 06.12.2026 09:30-12:00 | Grupo /CLE_01 | Medicine-Classroom 5 |
| 06.12.2026 09:30-12:00 | Grupo /CLE_01 | Medicine-Classroom 7 |
| 06.12.2026 09:30-12:00 | Grupo /CLE_01 | Medicine-Classroom 8 |