ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 30 Interactive Classroom: 24 Total: 55
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 (Extinguida)
Enrolment: No Matriculable | 1st year (Yes)
To know how to obtain, organize, present and describe data sets by means of descriptive statistics. To know how to calculate the probability of an event. To know the characteristics and distribution of random variables. To know how to describe and apply methods of inferential statistics.
Handle statistical packages that allow the analysis of data obtained in research in the field of Biology.
LECTURES
1. Descriptive statistics (5 hours)
General concepts. Frequency distributions. Graphical representations. Measures of position and dispersion of a variable. Two-dimensional descriptive statistics. Frequency distributions.
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.
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. Models of discrete distributions: Bernoulli and Binomial. Continuous random variable: density function and distribution function. Measures of position and dispersion of a random variable. Models of continuous distributions: The normal distribution. Approximation of distributions.
4. Estimation and confidence intervals (5 hours)
Introduction to statistical inference. General approach to the parametric inference problem. 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.
5. Hypothesis testing (5 hours)
The problem of hypothesis testing. Hypothesis testing for the proportion. Hypothesis testing for the mean and variance of a normal population. Comparison of two means in paired samples. Comparison of two means in independent samples.
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, 2 hours for each topic)
In the seminars there will be exercises related to each of the topics explained in the lectures.
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)
Follow-up of the development of the course and resolution of doubts.
BASIC BIBLIOGRAPHY
Milton, J.S. (2007): "Estadística para biología y ciencias de la salud", Mc Graw-Hill.
COMPLEMENTARY BIBLIOGRAPHY
Barón López F.J. (2021): "Bioestadística: Métodos y aplicaciones". Available at:
https://www.bioestadistica.uma.es/baron/bioestadistica.pdf [Accessed May 12, 2023]
Crujeiras, R.M. y Faraldo, P. (2010): "Manual de estadística básica para ciencias de la salud", Unidixital.
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 11, 2023]
Heumann, C.; Schomaker, M.; Shalab (2016): "Introduction to Statistics and Data Analysis", Springer. Available online (through the USC Library).
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, 2023]
Shahbaba, B. (2012): "Biostatistics with R", Springer. Available online (through the USC Library).
Verzani, J. (2002): "simpleR. Using R for Introductory Statistics". Available at:
https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf [Accessed May 12, 2023]
COMPETENCIES
CB2. That students know how to apply their knowledge to their work or vocation in a professional manner 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. Apply the theoretical-practical knowledge acquired in the approach of problems and the search for their solutions in both academic and professional contexts.
CG3. Know how to obtain and interpret relevant information and results and obtain conclusions in topics related to Biology.
CT1. Ability to search, process, analyze and interpret information.
CT2. Capacity for reasoning, argumentation and critical thinking.
CT7. Ability to apply "ICTs" in the field of Biology.
CT8. Ability to solve problems through the integrated application of their knowledge, promoting initiative and creativity.
CT10. Ability to interpret experimental results.
CE2. Propose, apply and interpret mathematical models and statistical methods in the field of Biology.
The lectures and seminars will be in the classroom with blackboard, where the theoretical contents of the subject and the procedures for solving the problems will be explained (solving exercises and proposing others to be solved by the students).
The laboratory classes can be taught in a computer classroom, or preferably, students could use their laptops. The computer tool R [http://www.r-project.org] will be introduced. Exercises will be solved and proposed to be solved with R by the students. This will allow us not only to put into practice the knowledge studied in the subject, but also to acquire the necessary resources to handle this computer tool.
Tests and assignments 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 to each topic, in which the student will use the statistical techniques and the knowledge acquired in the lectures.
Through this activity the following competences are evaluated: CB2, CG2, CG3, CT1, CT2, CT8, CT10, CE2.
-Evaluation of the laboratory.
This activity evaluates the following competences: CG2, CT1, CT2, CT7, CT8, CT10, CE2.
The grade obtained in the coursework will be kept in the two opportunities of the same course.
Final exam: the final exam will consist of several theoretical-practical questions on the contents of the subject, which may include the interpretation of results obtained with the computer tool R, used in the laboratory.
Through this activity the following competences are evaluated: CB2, CG2, CG3, CT1, CT2, CT8, CE2.
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 of the coursework (30%) and the grade in the theoretical-practical written exam (70%), on the other hand. Students who do not take the written theoretical-practical exam will have "not submitted".
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 of tutorials.
Attendance at all teaching activities.
Look up the recommended bibliography.
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.
This guide and the criteria and methodologies described in it are subject to modifications derived from the regulations and guidelines of the USC.
Indication referring to plagiarism and improper use of technologies in the performance of tasks or tests: For cases of fraudulent performance of exercises or tests, the provisions of the "Regulations for the evaluation of the academic performance of students and the review of grades" will apply.
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
Maria Alonso Pena
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaalonso.pena [at] usc.es
- Category
- Professor: Intern Assistant LOSU
| Monday | |||
|---|---|---|---|
| 18:00-19:00 | Grupo /CLE_01 | Spanish | Classroom 03. Carl Linnaeus |
| 19:00-20:00 | Grupo /CLE_02 | Spanish | Classroom 04: James Watson and Francis Crick |
| Tuesday | |||
| 18:00-19:00 | Grupo /CLE_01 | Spanish | Classroom 03. Carl Linnaeus |
| 19:00-20:00 | Grupo /CLE_02 | Spanish | Classroom 04: James Watson and Francis Crick |
| Wednesday | |||
| 18:00-19:00 | Grupo /CLE_01 | Spanish | Classroom 03. Carl Linnaeus |
| 19:00-20:00 | Grupo /CLE_02 | Spanish | Classroom 04: James Watson and Francis Crick |
| 05.23.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 01. Charles Darwin |
| 05.23.2024 16:00-20:00 | Grupo /CLE_02 | Classroom 01. Charles Darwin |
| 05.23.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 02. Gregor Mendel |
| 05.23.2024 16:00-20:00 | Grupo /CLE_02 | Classroom 02. Gregor Mendel |
| 05.23.2024 16:00-20:00 | Grupo /CLE_02 | Classroom 03. Carl Linnaeus |
| 05.23.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 03. Carl Linnaeus |
| 07.11.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 01. Charles Darwin |
| 07.11.2024 16:00-20:00 | Grupo /CLE_02 | Classroom 01. Charles Darwin |
| 07.11.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 02. Gregor Mendel |
| 07.11.2024 16:00-20:00 | Grupo /CLE_02 | Classroom 02. Gregor Mendel |