Biostatistics
Code: 100766 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2500250 Biology | FB | 1 | 1 |
Contact
- Name:
- Giulia Binotto
- Email:
- Giulia.Binotto@uab.cat
Use of Languages
- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No
Prerequisites
Although there are no official prerequisites, it is advisable for
the student to review: 1) Combinatorics and Newton's binomial. 2)
The probability and the statistics that have studied in the
Baccalaureate. 3) The elementary functions (exponential, logarithm),
summations.
Objectives and Contextualisation
Contextualization:
This is a basic, instrumental type course that introduces probabilistic tools and basic statistics in Biology studies in order to analyze biological data from the description of natural phenomena or experiments. These tools will be used for other subjects of the degree and are essential for the future graduate in Biology training both for the pursuit of their profession and for research. Along with Mathematics, this is characterized by the fact that in addition to its own content, it helps the students to develop scientific rigor and logical thinking.
Training objectives of the subject: It is intended that the student...
- Be able to use fluently the language of the probability and the statistics used in Biology.
- Learn how to explore descriptive methods with various sets of data, resulting from the observation of biological phenomena or experimentation.
- Be able to raise the most suitable probabilistic models in different situations, and know how to use the probability rules to calculate the probability of the events of interest.
- Know and understand the concept of random variable, know classical examples of random variables and in what situations are used for modeling.
- Learn how to use the methods of statistical inference (confidence intervals and hypothesis tests) to reach conclusions on one or more populations based on partial information contained in random samples.
- Know computer tools (R software and R Commander user graphical interface) for the statistical treatment of data.
- Apply common sense and develop a critical spirit in dealing with the problems that will have to be solved, both at the time of its resolution and resolution, as well as at the time of drawing conclusions and making decisions.
Competences
- Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values.
- Apply statistical and computer resources to the interpretation of data.
- Be able to analyse and synthesise
- Make changes to methods and processes in the area of knowledge in order to provide innovative responses to society's needs and demands.
- Obtain information, design experiments and interpret biological results.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Take account of social, economic and environmental impacts when operating within one's own area of knowledge.
- Take sex- or gender-based inequalities into consideration when operating within one's own area of knowledge.
- Understand, interpret and use mathematical and statistical tools to solve problems in biology.
- Work in teams.
Learning Outcomes
- Analyse a situation and identify its points for improvement.
- Apply statistical and computer resources to the interpretation of data.
- Be able to analyse and synthesise.
- Critically analyse the principles, values and procedures that govern the exercise of the profession.
- Design experiments based on knowledge of statistics.
- Identify and interpret the statistical tools that can be used to solve problems in biology.
- Obtain information from experimental data, present it correctly and interpret it.
- Propose new methods or well-founded alternative solutions.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Take account of social, economic and environmental impacts when operating within one's own area of knowledge.
- Take sex- or gender-based inequalities into consideration when operating within one's own area of knowledge.
- Use statistical tools to solve problems in biology.
- Work in teams.
Content
1. Descriptive statistics.
(Part of the topics of this chapter will be developed in practice classes with statistics software)
- Data and random error. Measurement scales.
- Descriptive analysis of data from a single variable: frequency distributions, graphic representations, numerical summaries (position, dispersion and shape measurements).
- Descriptive analysis of data from two variables: correlation and regression line, tables of contingency.
- R software and R Commander graphical user interface.
2. Probability.
- Basic properties of probability. Conditional probability. Formula of total probabilities. Bayes Formula. Independence of events.
- Expectation and variance of a random variable.
- Discrete random variables: Bernoulli, Binomial and Hypergeometric.
- Continuous random variables: Normal distribution. Approximation of the Binomial by the Normal distribution.
- Independence of random variables.
3. Statistical inference.
(Part of the topics of this chapter will be developed in practice classes with statistics software)
- Introduction to Statistics: population and sample, parameters and estimators. Distribution of the mean sample in the normal case with known variance: Z-statistic. Confidence interval for the mean of a normal population with known variance.
- Student's distribution. The case of the unknown variance: the T-statistic and the confidence interval for the mean of a normal population with unknown variance.
- The sample proportion of a population characteristic. Asymptotic confidence interval for the proportion.
- Introduction to hypothesis tests. Hypothesis test for the mean of the normal with known variance and with unknown variance. Tests for the population proportion.
- Tests to compare two normal populations. The test of comparison of variances. The case of two dichotomous populations.
- The Shapiro-Wilk test of normality. Non-parametric tests forthe comparison of means.
- The chi square tests of goodness of adjustment of independence.
- Hypothesis tests to compare more than two normal populations: introduction to the Analysis of Variance (ANOVA).
*Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.
Methodology
The center of the learning process is the work of the student. The student learns working, being the mission of the teaching staff help him/her in this task by providing information or showing him/her the sources where one can get it and directing your steps in a way that the learning process can be carried out effectively. In line with these ideas, and in accordance with the objectives of the subject, the course development is based on the following activities:
Theory classes:
The student acquires the scientific-technical knowledge of the subject assisting the theory classes, complementing them with self-study of the subjects explained in order to assimilate the concepts and the procedures, to detect doubts and to realize summaries and schematics of the subject. In the theory classes the teacher introduces the basic concepts corresponding to the subject of the subject, showing their own application They are made with slate and with the support of ICT.
Problems and practices:
The problems and the practices are sessions with a smaller number of students where the scientific-technical knowledge is worked exposed in theory classes to complete their understanding and deepen it by solving problems and cases Practical, with the appropriate software. The students will work on individually or in group, under the supervision of the teacher, solving the proposed problems. This will be done both in class and at autonomous way by the student.
There will be 12 sessions of one hour (for each group) of problems and 4 Practical sessions with a computer, to which the student will learn to use the free R software with the graphical user interface R Commander in order to apply the statistical tools for the analysis descriptive of sets of data and the statistic inference.
*The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Classes of theory | 16 | 0.64 | 15, 14, 4, 1, 2, 5, 6, 7, 8, 13, 12, 11, 9, 10, 3, 17, 16 |
| Classes of theory, problems and practice | 32 | 1.28 | 15, 14, 4, 1, 5, 6, 7, 8, 13, 12, 10, 3, 16 |
| Type: Supervised | |||
| Individual Tutorials | 10.5 | 0.42 | 15, 14, 4, 1, 2, 5, 6, 7, 8, 13, 12, 11, 9, 10, 3, 16 |
| Type: Autonomous | |||
| Study + work of problems and practices | 83.5 | 3.34 | 15, 14, 4, 1, 2, 5, 6, 7, 8, 13, 12, 11, 9, 10, 3, 17, 16 |
Assessment
The evaluation of the subject consists of a part of continuous evaluation of the acquired competences: there will be a partial eliminatory examination with a weight of 35%, there will also be a second eliminatory partial, with a weight of 40%. These two partials will be the recoverable part of the subject.
The evaluation of the practices with computer, will have a weight of 25% in the final evaluation of the subject. The note of the practice part will be obtained from exam of exercises solved using statistical software.
To participate in the recovery examination, the students must have been previously evaluated in a series of activities whose weight equals to a minimum of 2/3 of the total grade of the subject. Therefore, the students will obtain the "Non-evaluable" qualification when the evaluation activities carried out have a weighting of less than 67% in the final grade.
*Student's assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Partial exams | 75% | 5 | 0.2 | 15, 14, 4, 1, 2, 5, 6, 7, 8, 13, 12, 11, 9, 10, 3, 16 |
| Practice exam | 25% | 1.75 | 0.07 | 15, 14, 4, 1, 2, 5, 6, 7, 8, 13, 12, 11, 9, 10, 3, 16 |
| Recovery exam | 75% | 1.25 | 0.05 | 15, 14, 4, 1, 2, 5, 6, 7, 8, 13, 12, 11, 9, 10, 3, 17, 16 |
Bibliography
Bibliografy
Besalú, M. Rovira C. Probabilitats i estadística. Publicacions i Edicions de la Universitat de Barcelona, 2013.
Delgado, R.: Probabilidad y Estadística para ciencias e ingenierías. Delta, Publicaciones Universitarias. 2008.
Devore, Jay L. Probabilidad y Estadística para ingeniería y ciencias. International Thomson Editores. 1998.
Milton, J. S.: Estadística para Biología y Ciencias de la Salud, Interamericana de España, McGraw-Hill, 1994 (2a ed.).
Remington, R. D. Schork, M. A.: Estadística Biométrica y Sanitaria, Prentice/Hall Internacional, 1974.