ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 91 Hours of tutorials: 3 Expository Class: 30 Interactive Classroom: 26 Total: 150
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: Sin docencia (Extinguida)
Enrolment: No Matriculable | 1st year (Yes)
The main objective of this subject is for students to become familiar with the basic concepts and techniques of Descriptive Statistics, Probability and Statistical Inference.
In addition, it is intended that students understand the need and utility of statistical methodology in research in Health Sciences, particularly in Dentistry, becoming aware of the scope and limitations of this methodology.
As more specific objectives, students must:
- Know the basic statistical language
- Know and apply some basic statistical methods to represent and analyse simple data sets, and be able to draw conclusions from those analysis.
- Know, express and correctly interpret the levels of precision, confidence and error levels in the conclusions of a statistical study.
Topic 1. Descriptive Statistics
Introduction to Statistics. Variables. Frequency distributions. Graphic representations. Position and dispersion measurements.
Topic 2. Probability
Random experiment. Sample space. Events. Conditioned probability. Independence of events. Product rule, law of total probabilities and Bayes' theorem. Sensitivity, specificity, positive and negative predictive values.
Topic 3. Random variables
Discrete and continuous random variables. Probability distributions: mass function, density function and distribution function. Measures of a random variable. The Binomial distribution and the Normal distribution. Approximation of the Binomial distribution by the Normal distribution.
Topic 4. Estimation and confidence intervals
Introduction to Statistical Inference. Parameter estimation. Confidence intervals for the proportion and for the mean and variance of a Normal population.
Topic 5. Hypothesis testing
Introduction. Null and alternative hypothesis. Types of errors in a hypothesis test. Level of significance and power of a test. Stages in the resolution of a hypothesis test. The critical level or p-value. Tests with a sample: test for the proportion, and for the mean and variance of a Normal population. Tests with two samples: comparison of two means (independent samples, paired samples).
Unit 6.- Association of categorical variables.
Introduction. Crosstabs. Contrasts for categorical data: Chi-square test.
Topic 7. Regression models
Introduction to regression models. General concepts. The simple linear model. Parameter estimation: the least squares method. Inference about parameters. Decomposition of variability. Correlation coefficient and determination coefficient. Prediction.
Cao, R. et al. (1998). Estadística básica aplicada. Tórculo Edicións.
Daniel, W.W. (2002): “Bioestadística. Base para el análisis de las ciencias de la salud”, Limusa Wiley.
García García, V.J. (2014). Estadística descriptiva y Probabilidad. Universidad de Cádiz. At https://prelo.usc.es/.
Kim, J.S. and Dailey, R.J. (2008): "Biostatistics for Oral Healthcare", Blackwell.
Martín Andrés, A. and Luna del Castillo, J. (2004): “Bioestadística para las ciencias de la salud”, Norma.
Milton, J.S. (2007): “Estadística para Biología y Ciencias de la Salud”, McGraw-Hill-Interamericana.
Montero Fernández, J. and Minuesa Abril, C. (2018). Estadística básica para Ciencias de la Salud. Universidad de Extremadura. At http://matematicas.unex.es/~jmf/Archivos/Manual%20de%20Bioestad%C3%ADst….
Pagano, M and Gauvreau, K. (2001): “Fundamentos de Bioestadística”, Thomson-Learning.
Paradis, E. (2003). R para principiantes. R Cran. At https://cran.r-project.org/doc/contrib/rdebuts_es.pdf.
Recursos TIC del Ministerio de Educación, Cultura y Deporte (Proyecto Descartes). http://recursostic.educacion.es/descartes/web/materiales_didacticos/Mue… or http://recursostic.educacion.es/descartes/web/materiales_didacticos/inf….
Requena, F. (2008): “Introducción a la Estadística: Aplicación a la Odontología”, Proyecto Sur.
Rosner, B. (2011): “Fundamentals of Biostatistics”, Duxbury Press.
Smeeton N. (2005): “Dental Statistics Made Easy”, Radcliffe Publishing (Oxford).
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). At https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
Within this matter, the aim will be to help students achieve the competencies included in the memory of the USC Bachelor's Degree in Dentistry:
BASIC
• CG.07. - Promote autonomous learning of new knowledge and techniques, as well as motivation for quality.
• CG.08. - Know how to share information with other health professionals and work as a team.
• CG.09. - Understand the importance of maintaining and using records with patient information for subsequent analysis, preserving the confidentiality of the data.
• CG.18. - Know, critically value and know how to use the sources of clinical and biomedical information to obtain, organize, interpret and communicate scientific and health information.
• CG.19. - Know the scientific method and have a critical capacity to value the established knowledge and novel information. Being able to formulate hypotheses, collect and critically assess information for problem solving, following the scientific method
SPECIFIC
• CEMII.01. - Know the scientific method and have a critical capacity to assess established knowledge and novel information.
• CEMII.05. - Know the procedures and clinical and laboratory diagnostic tests, know their reliability and diagnostic validity and be competent in interpreting their results.
Lectures (30 hours): in the lectures, the teachers will explain the theoretical-practical concepts of the contents, relying on multimedia presentations. Some typical problems will also be solved, so that the students can work on the exercises that will be provided. Regarding the material for the monitoring of the subject, in spite of the recommended bibliography, the students will have complementary teaching material through the Virtual Campus of the subject. In the lectures the general competences CG.07, CG.18, CG.19 as well as the specific CEMII.01 will be worked on.
Interactive teaching (26 hours): interactive teaching is distributed in exercises solving seminars and computer practices. In these sessions, students will be introduced to the handling of the R package for statistical data analysis. We will also work on the practical cases that will have to be solved. To monitor computer sessions, students will be provided with the dossiers of the practices. In the interactive teaching sessions, the following competences will be enhanced: CG.07, CG.08, CG.09 and CEMII.05.
Tutorials: the tutorials are aimed at monitoring student learning. They will be carried out both in person and through email or MS Teams. In these sessions, fundamental skills related to critical thinking and communication skills will be worked on.
The methodology will be adapted as follows to the different possible scenarios:
SCENARIO 1 (adapted normality)
The lectures and interactive teaching will be face-to-face and will be complemented by the Virtual Campus of the subject, in which the students will find bibliographic materials, problems, explanatory videos.... Through the Virtual Campus, students will also be able to carry out tests and tasks for continuous assessment.
SCENARIO 2 (distancing)
Partially virtual teaching, according to the distribution organized by the Faculty of Medicine and Dentistry. For this, the Virtual Campus of the course will be used, with explanatory videos, bibliographic materials and complementary material for the practical sessions provided by the teacher, as well as synchronous sessions through MS Teams. The tutorials will be attended only by email or through MS Teams.
SCENARIO 3 (closure)
Completely virtual teaching through the Virtual Campus of the subject. Students must periodically present tasks related to the contents covered that will serve to carry out the continuous assessment.
Continuous assessment (30%): continuous assessment will be carried out based on participation in two types of tasks: those associated with interactive seminar sessions (problem solving individually or in groups) and those associated with interactive laboratory sessions (resolution of practical exercises with the statistical program R). These activities can be carried out both in person and in person. To opt for continuous assessment, it is necessary to attend at least 75% of the practical sessions. In specific cases in which the student duly justifies to the teaching staff the impossibility of attending the computer practice sessions, a series of alternative tasks will be proposed to be carried out throughout the course to be eligible for continuous assessment.
Final exam (70%): the final exam will consist of several theoretical-practical questions about the contents of the subject, which may include the interpretation of results obtained with the statistical package used in interactive teaching.
The weight of the continuous evaluation in the recovery opportunity will be the same as in the ordinary call for the semester.
The final grade of the subject will never be lower than the final exam grade or the grade obtained by weighting 30% of the continuous assessment and 70% of the final exam grade.
SCENARIO 1 (adapted normality)
Both continuous assessment tests and the final exam will be done in person, with the exception of those continuous assessment tasks that are carried out outside the classroom. To carry out the final exam, the calendar proposed by the Faculty will be followed and the regulations issued by the USC will be respected at all times.
SCENARIO 2 (distancing)
In scenario 2, the evaluation system will be the same as the one described in scenario 1 and an attempt will be made to keep the tests in which the conditions required by USC regulations can be met. If the controls that are part of the continuous evaluation, as well as the final exam, could not be attended, they would be non-presential. To carry out these tests, the Virtual Campus would be used to download the exam and deliver it once completed by the student in a limited time. The supervision of the tests will be carried out through the corporate platform MS Teams: the students will have to be connected during the test to the session that each group will have with the teachers. Supervision will not be recorded, but teachers may request, if they deem it appropriate, the display of an identification document.
SCENARIO 3 (closure)
In scenario 3, the evaluation system will be the same as that described in scenario 1 and all the tests will be non-face-to-face. These tests will be as described in scenario 2.
Note that, in cases of fraudulent performance of exercises or tests, the provisions of the "Regulations for the evaluation of student academic performance and the review of grades" will apply.
In this matter, students have 56 hours of face-to-face teaching (30 hours of expository teaching and 26 hours of interactive teaching). For each hour of expository teaching, it is considered necessary to dedicate around 1.5 hours of student work to the study (review of concepts and bibliography consultation).
In relation to interactive teaching, for each hour one hour is considered necessary for class review. Older students should bear in mind that it is necessary to practice problem solving (from the bulletins or from the recommended bibliography).
In this matter, students have 56 hours of face-to-face teaching (30 hours of expository teaching and 26 hours of interactive teaching). For each hour of expository teaching, it is considered necessary to dedicate around 1.5 hours of student work to the study (review of concepts and bibliography consultation).
SCENARIO 1 (adapted normality)
Attendance at expository and interactive sessions is essential for monitoring and passing the subject. Students must carry out all the activities recommended by the teaching staff (problem solving, bibliography review and practical exercises) to successfully pass the subject.
SCENARIO 2 (distancing)
Attendance at expository and interactive sessions (both face-to-face and those carried out synchronously through MS Teams) is essential for monitoring and passing the subject. In addition, the contents of those interactive sessions that are not done synchronously must be worked on during that time by the student with the help of the adapted material provided by the teacher.
Students must carry out all the activities recommended by the teaching staff (problem solving, bibliography review and practical exercises) to successfully pass the subject.
SCENARIO 3 (closure)
Virtual attendance at the synchronous interactive and expository sessions that will be held through MsTeams is essential for monitoring and passing the subject. In addition, the contents of those interactive sessions that are not done synchronously must be worked on during that time by the student with the help of the adapted material provided by the teacher.
Students must carry out all the activities recommended by the teaching staff (problem solving, bibliography review and practical exercises) to successfully pass the subject.
Contingency Plan
1. In the event that it is not possible to teach face-to-face, it will be replaced by synchronous teaching through the MS Teams platform (following the schedules established by the center), complemented by guidelines, explanatory videos and exercises solved in the Virtual Campus .
2. The exams and tests for continuous evaluation will be face-to-face or telematic depending on the setting.
3. This guide and the criteria and methodologies described therein are subject to modifications derived from USC regulations and guidelines.
Maria Isabel Borrajo Garcia
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaisabel.borrajo [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
| Monday | |||
|---|---|---|---|
| 09:30-10:30 | Grupo /CLE_01 | Galician | Dentistry-Assembly Hall |
| Tuesday | |||
| 09:30-10:30 | Grupo /CLE_01 | Galician | Dentistry-Assembly Hall |
| 12:30-13:30 | Grupo /CLIL_01 | Galician | Dentistry-Assembly Hall |
| Wednesday | |||
| 11:30-13:30 | Grupo /CLIL_01 | Galician | Dentistry-Assembly Hall |
| 01.21.2021 16:00-18:00 | Grupo /CLE_01 | Dentistry-A. Suárez Nuñez |
| 01.21.2021 16:00-18:00 | Grupo /CLE_01 | Dentistry-Classroom 3 |
| 07.01.2021 16:00-18:00 | Grupo /CLE_01 | Dentistry-A. Suárez Nuñez |
| 07.01.2021 16:00-18:00 | Grupo /CLE_01 | Dentistry-Classroom 3 |