ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 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 Biology
Call: Second Semester
Teaching: Sin Docencia (En Extinción)
Enrolment: No Matriculable (Sólo Planes en Extinción)
Get familiar with the essential techniques of Statistics and its application in the field of Biology.
Learn to manage a statistical package that allows the analysis of data obtained in research in the field of Biology.
LECTURES
Topic 1.- Descriptive statistics (5 hours)
General concepts. Frequency distributions. Graphic representations. Position and dispersion measures of a variable. Two-dimensional descriptive statistics. Frequency distributions.
Topic 2.- Foundations of probability (4 hours)
Random experiment. Events and sample space. Conditional probability. Independence of events. Product rule, law of total probabilities and Bayes' theorem. Applications in Biology.
Topic 3.- Random variables (6 hours)
Discrete random variable: probability mass function and distribution function. Position and dispersion measures of a random variable. Distribution of two-dimensional variables. Independence of random variables. Discrete distribution models: Bernoulli and Binomial. Continuous random variable: density function and distribution function. Characteristic measures. Models of continuous distributions: The normal distribution. Approximation of distributions.
Topic 4.- Estimation and confidence intervals (5 hours)
Introduction to statistical inference. General approach to the problem of parametric inference. Point estimate of a proportion. Bias and variance of an estimator. Concept of confidence interval. Confidence interval for a proportion. Point estimate of the mean and variance of a normal population. Confidence intervals for the mean and variance of a normal population.
Topic 5.- Hypothesis testing (5 hours)
The problem of hypothesis testing. hypothesis testing for the population proportion. Hypothesis testing for the mean and the variance of a normal population. Comparison of two means in paired samples. Comparison of two means in independent samples.
Topic 6.- The simple linear regression model (5 hours)
Elements of a regression model: the linear model. Estimation of the model parameters. Inference about parameters. Covariance, correlation coefficient and determination coefficient. Decomposition of variability. The F test. Prediction.
SEMINARS (12 hours)
Exercises related to each of the topics explained in the lectures will be done in seminars.
LABORATORY (12 hours)
Introduction to R. (2 hours)
Univariate descriptive statistics (2 hours)
Bivariate descriptive statistics. Probability distribution models (2 hours)
Estimation and confidence intervals (2 hours)
Hypothesis testing (2 hours)
Simple linear regression (2 hours)
TUTORIALS (1 hour)
Monitoring of course development and resolution of doubts.
Students will have the notes of the subject in the Virtual Campus. In these notes are all the contents (theoretical and practical) of the subject.
Books of which students can have a digital copy:
Through the portal of the Library of the University of Santiago de Compostela, books available on Springerlink:
Heumann, C .; Schomaker, M .; Shalab (2016): “Introduction to Statistics and Data Analysis”, Springer.
Shahbaba, B. (2012): "Biostatistics with R", Springer.
Francisco Javier Barón López book (University of Malaga), available at the address:
https://www.bioestadistica.uma.es/baron/apuntes/clase/apuntes/pdf/bioes…
Jesús Montanero Fernández book (University of Extremadura), available at the address:
http://matematicas.unex.es/~jmf/Archivos/Bioestadistica.pdf
Verzani, J. (2002): “simpleR”, available at:
https://www.math.csi.cuny.edu/Statistics/R/simpleR/printable/
Glover, T .; Mitchell, K. (2016): ”An Introduction to Biostatistics using R”, Waveland Press, available at:
http://waveland.com/Glover-Mitchell/r-guide.pdf
Books of which students can have a printed copy in the Library of the University of Santiago de Compostela:
Crujeiras, R.M. and Faraldo, P. (2010): “Manual of basic statistics for health sciences”, Unidixital.
Martínez González, M.A .; Sánchez Villegas, A .; Toledo Atucha, E .; Faulin Fajardo, J. (2020): "Friendly Biostatistics", Elsevier.
Milton, J.S. (2007): "Statistics for Biology and Health Sciences, Mc Graw-Hill.
In this subject, the student will practice basic, general and cross-sectional skills in the Degree in Biology, and specific skills of this particular subject. Specifically, the following skills are worked in this subject:
Basic and general skills
CB2. That the students know how to apply their knowledge to their work or vocation in a professional way and possess the competences that are usually demonstrated through the elaboration and defense of arguments and the resolution of problems within their area of study.
CG2. Gather and interpret relevant data, information and results, obtain conclusions and issue reasoned reports on problems related to Biology.
CG3. Apply both the theoretical-practical knowledge acquired and the capacity for analysis and abstraction in the definition and approach of problems and in the search for solutions in both academic and professional contexts.
Cross-sectional skills
CT1. Analysis and synthesis capacity.
CT2. Ability for reasoning and argumentation.
CT5. Ability to prepare and present an organized and understandable text.
CT7. Commitment to the veracity of the information it offers to others.
Specific skills
Knowledge of the use of different statistical techniques with a view to solving problems related to life sciences. The student must acquire sufficient knowledge to:
CE1. Do a descriptive reading of the data you have about a real situation.
CE2. Propose in each real situation the most appropriate statistical analysis taking into account the previous information and the objectives to be achieved.
CE3. Interpret the results of the statistical analysis based on the objectives
proposed.
CE4. Manage computer programs with a view to numerical resolution, with the appropriate technique, of the problem set out.
In scenario 1 (adapted normality), the lectures and seminars will be in a classroom with a blackboard, where the theoretical contents of the subject and the problem-solving procedures will be explained (solving exercises and proposing others for their resolution by the students).
The laboratory classes may be taught in the computer room or, failing that, the students may use their laptops. The computer tool R [http://www.r-project.org] will be introduced. The language R will be introduced, and exercises will be solved and proposed for their resolution with R by the students. This will allow us not only to put into practice the knowledge studied in the matter, but also to acquire the necessary resources to handle the computer tool.
In scenario 1, the tests will be face-to-face.
The following scheme will be maintained:
Coursework: the coursework will be carried out throughout the semester. It will consist of the following elements:
-Resolution of exercises and questions associated with each topic, in which the student will use statistical techniques and the knowledge acquired in the lectures.
Through this activity the following skills are evaluated: CB2, CG2, CG3, CT1, CT2, CT5, CT7, CE1, CE2, CE3.
-Questionnaires for evaluating laboratory work.
Through this activity the following competences are evaluated: CG2, CE1, CE3, CE4.
The grade obtained in the coursework will be kept in both opportunities of the same course.
Final exam: 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 R computer tool, used in laboratory..
Through this activity the following competences are evaluated: CB2, CG2, CG3, CT1, CT2, CT5, CT7, CE1, CE2, CE3, CE4.
The final grade, both in the first and in the second opportunity, will be the maximum of the grade in the theoretical-practical written exam, on the one hand, and the weighted average between the continuous assessment (30%) and the grade in the theoretical-practical written exam (70%), on the other hand.
Students who do not take the theoretical-practical written exam will have "no presentado".
In scenario 1, the tests and the homework will be face-to-face.
It is recommended to dedicate at least an hour and a half of additional work for each hour of expository and interactive class, in addition to the hours of tutorials.
Attendance at all teaching activities.
Look up the recommended bibliography.
This guide and the criteria and methodology described here are subject to the modifications arising from the regulations and guidelines of the University of Santiago de Compostela.
The students will have the material of the subject in the Virtual Campus (Moodle), and the MS Teams tool for communication with the teaching staff.
CONTINGENCY PLAN IF REQUIRED BY THE HEALTH SITUATION:
In accordance with the indications established by the academic authorities, the methodology and evaluation will be adapted to the scenarios 2 and 3.
TEACHING METHODOLOGY
In scenario 2 (distancing, partial restrictions on physical presence), keep face-to-face the lectures, seminars and tutorials, as long as the required conditions are met.
If it were not possible to teach the lectures in a large classroom, they would become online and synchronous, using the telematic tools that the University of Santiago de Compostela makes available to the university community (MS Teams). The material of the subject would be available on the Virtual Campus and in addition the students would have supplementary videos of the subject, available in MS Stream, on the channel of the subject created for this purpose.
If it were not possible to keep the seminars in person, they would become online and synchronous, using the telematic tools that the University of Santiago de Compostela makes available to the university community (MS Teams). The course material would be available on the Virtual Campus and also the students would have solved exercises.
If it was not possible to keep the laboratory classes in person, they would become online and synchronous, using the telematic tools that the University of Santiago de Compostela makes available to the university community (MS Teams). In addition to the material for the practical classes, already available on the Virtual Campus, the students would have supplementary R scripts that would help them to carry out the assigned tasks.
If it was not possible to keep the tutorials in person, they would become online, using the telematic tools that the University of Santiago de Compostela makes available to the university community (Moodle, MS Teams).
In scenario 3 (closure of the facilities), all teaching would be online. The means used in this case would be those already exposed in scenario 2.
ASSESSMENT SYSTEM
In scenario 2, keep in-person the tests in which the conditions required by the University regulations can be met.
If the tests that are part of the continuous evaluation could not be face-to-face, they would be online and synchronous.
The tests would be similar to those that would be carried out in person, using the telematic tools that the University of Santiago de Compostela makes available to the university community for its execution and supervision (Moodle, MS Teams). They would be held on a fixed schedule, the same for all students, and with limited time. The Virtual Campus would be used to download the test to do and upload it, once completed by the student.
If the first opportunity exam could not be face-to-face, it would be online and synchronous, using the telematic tools that the University of Santiago de Compostela makes available to the university community (Moodle, MS Teams). The exam would be similar to the one that would be carried out in person, using the telematic tools that the University of Santiago de Compostela makes available to the university community for its execution and supervision. They would be held on a fixed schedule, the same for all students, and with limited time. The Virtual Campus will be used to download the exam and upload it, once completed by the student.
It would be the same in the event that the second chance exam could not be face-to-face.
In scenario 3, all the tests would be online and synchronous, as described in scenario 2.
Indication regarding plagiarism and the improper use of technologies in the performance of tasks or tests: In cases of fraudulent completion of exercises or tests, the provisions of the "Regulations for the evaluation of academic performance of students and the review of the qualifications ” will be applied.
Pedro Faraldo Roca
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813216
- pedro.faraldo [at] usc.es
- Category
- Professor: University Lecturer
Maria Angeles Casares De Cal
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813183
- mariadelosangeles.casares.decal [at] usc.es
- Category
- Professor: University Lecturer
Paula Saavedra Nieves
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- paula.saavedra [at] usc.es
- Category
- Professor: Temporary PhD professor
Alejandro Saavedra Nieves
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- alejandro.saavedra.nieves [at] usc.es
- Category
- Professor: Temporary supply professor to reduce teaching hours
Fernando Castro Prado
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- f.castro.prado [at] usc.es
- Category
- Ministry Pre-doctoral Contract
| Monday | |||
|---|---|---|---|
| 18:00-19:00 | Grupo /CLE_02 | Spanish, Galician | Classroom 04: James Watson and Francis Crick |
| 19:00-20:00 | Grupo /CLE_01 | Spanish | Classroom 03. Carl Linnaeus |
| Tuesday | |||
| 18:00-19:00 | Grupo /CLE_02 | Galician, Spanish | Classroom 04: James Watson and Francis Crick |
| 19:00-20:00 | Grupo /CLE_01 | Spanish | Classroom 03. Carl Linnaeus |
| Wednesday | |||
| 18:00-19:00 | Grupo /CLE_02 | Galician, Spanish | Classroom 04: James Watson and Francis Crick |
| 19:00-20:00 | Grupo /CLE_01 | Spanish | Classroom 03. Carl Linnaeus |
| 05.30.2022 16:00-20:00 | Grupo /CLE_02 | Classroom 01. Charles Darwin |
| 05.30.2022 16:00-20:00 | Grupo /CLE_01 | Classroom 01. Charles Darwin |
| 05.30.2022 16:00-20:00 | Grupo /CLE_01 | Classroom 02. Gregor Mendel |
| 05.30.2022 16:00-20:00 | Grupo /CLE_02 | Classroom 02. Gregor Mendel |
| 05.30.2022 16:00-20:00 | Grupo /CLE_01 | Classroom 03. Carl Linnaeus |
| 05.30.2022 16:00-20:00 | Grupo /CLE_02 | Classroom 03. Carl Linnaeus |
| 07.13.2022 16:00-20:00 | Grupo /CLE_01 | Classroom 01. Charles Darwin |
| 07.13.2022 16:00-20:00 | Grupo /CLE_02 | Classroom 01. Charles Darwin |
| 07.13.2022 16:00-20:00 | Grupo /CLE_01 | Classroom 02. Gregor Mendel |
| 07.13.2022 16:00-20:00 | Grupo /CLE_02 | Classroom 02. Gregor Mendel |