| Competences |
| Type A | Code | Competences Specific |
| CE9 | Capacity to apply statistical tests and multivariate analysis algorithms to clinical, omic and biochemical data and data from other sources. | |
| Type B | Code | Competences Transversal |
| Type C | Code | Competences Nuclear |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| CE9 |
Understand the basic concepts of statistics Understand the different types of random variable distributions Apply the correct statistical models and algorithms biology Understand the theory and statistics associated with experimental design Determine the likelihood or probability of the conclusions that can be extracted from data and rule out unlikely occurrences Use multivariant data analysis techniques to visualise trends or possible erroneous measurements Can apply data correlation techniques to reveal interelations between different biological processes Can apply various techniques to obtain holistic conclusions about experiments with a lot of data and/or variables Understand and apply multivariant or pattern recognition algorithms to predict variables on the basis of the data from a laboratory experiment Can calibrate diagnostic tests based on multivariant techniques, pattern recognition, artificial intelligence or machine learning | |
| Type B | Code | Learning outcomes |
| Type C | Code | Learning outcomes |
| Contents |
| Topic | Sub-topic |
| Introduction | |
| El llenguatge R | |
| Data Exploration | |
| Exploring variable relationships | |
| Probability | |
| Random Variables and Probability Distributions | |
| Estimation | |
| Hypothesis Testing | |
| Statistical Inference for the Relationship Between Two Variables | |
| Analysis of Variance (ANOVA) | |
| Analysis of Categorical Variables | |
| Regression Analysis | |
| Cluster analysis | |
| Bayesian analysis | |
| Multivariate Analysis |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 0 | 1 | ||||||
| Lecture |
|
28 | 42 | 70 | ||||||
| Laboratory practicals |
|
22 | 36 | 58 | ||||||
| IT-based practicals |
|
8 | 12 | 20 | ||||||
| Personal attention |
|
1 | 0 | 1 | ||||||
| (*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||||||||
| Methodologies |
| Description | |
| Introductory activities | Descriptive activity of the course subject and organization |
| Lecture | Explanation in the classroom of the theoretical contents of the subject |
| Laboratory practicals | Carrying out of statistical analysis in the field of biomedical engineering, using the tools described in the lectures |
| IT-based practicals | Use of software and environments such as Matlab and R to perform statistical analysis and data treatment in the context of biomedical engineering. |
| Personal attention | Tuition with the lecturers of the subject. |
| Personalized attention |
| Description |
| It is recommended to use the timetable specifically designated by lecturers, to solve doubts or deal with any aspect related to the subject. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| Lecture |
|
Resolution of 2 partial tests consisting of tests with objective answers. |
50 |
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| Laboratory practicals |
|
Resolution of the practical work in the laboratory. Writing asummarizing reports | 25 | ||||
| IT-based practicals |
|
Final test of the part related to lab sessions and the use of the R language | 25 | ||||
| Others |
| Other comments and second exam session | |||
|
You must get a minimum score of 4 from each of the parts to be able to calculate the final grade. If you get less than 4 in any of the parts, it is considered not suitable. The attendance at the laboratory sessions is necessary in order to pass the subject. The lack of attendance and work in the laboratory is not recoverable in the second call. |
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| Sources of information |
| Basic |
Babak Shahbaba, Biostatistics with R: An Introduction to Statistics Through Biological Data, DOI 10.1007/978-1-4614-13, Springer, 2012
Wayne W. Daniel, Biostatistics: Basic concepts and Methodology for the Heath Sciences, ISBN: 978-0-470-41333-3, Wiley, 2010 |
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The complementary bibliography will be announced at the presentation of the course. |
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| Complementary | |||||||||
| Recommendations |
| Subjects that continue the syllabus | ||
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| Subjects that it is recommended to have taken before | ||||
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| Other comments | |
| The subjects of the same area that continue the syllabus in subsequent semesters are: - Digital processing of biosignals - Processing of biomedical images - Omic technologies and data processing - Computational and analytical biology of biomedical data |
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.