Study programme competencies |
Code
|
Study programme competences / results
|
A21 |
Deseñar modelos de procesos biolóxicos. |
A26 |
Deseñar experimentos, obter información e interpretar os resultados. |
A30 |
Manexar adecuadamente instrumentación científica. |
B2 |
Resolver problemas de forma efectiva. |
B3 |
Aplicar un pensamento crítico, lóxico e creativo. |
B6 |
Organizar e planificar o traballo. |
B10 |
Exercer a crítica científica. |
Learning aims |
Learning outcomes |
Study programme competences / results |
To design experiments, to get information and to explain the results
|
A21 A26 A30
|
B2 B3 B10
|
|
To apply an inquisitive, logical and creative reasoning to solving problems effectively.
|
|
B2 B3 B6
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|
Contents |
Topic |
Sub-topic |
Probability Theory |
Basic concepts on probability theory
Random variables
Basic probability distributions in Biology |
Descriptive Statistics |
Describing univariate data
Describing bivariate data |
Statistical Inference |
Introduction
Point estimation
Interval estimation
Parametric hypothesis testing of one and several samples
Nonparametric hypothesis testing of one and several samples
|
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Short answer questions |
A21 B2 B3 B6 |
2 |
0 |
2 |
ICT practicals |
A30 A26 B2 B3 B6 B10 |
13 |
26 |
39 |
Problem solving |
A21 B2 B3 B6 B10 |
8 |
19.2 |
27.2 |
Guest lecture / keynote speech |
A21 A26 B2 B3 B10 |
24 |
50.4 |
74.4 |
Supervised projects |
A26 |
0.5 |
1.9 |
2.4 |
Objective test |
A26 A30 B2 B3 B10 |
3 |
0 |
3 |
|
Personalized attention |
|
2 |
0 |
2 |
|
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Short answer questions |
Short answer and/or test questions with the aim of controlling the progress in the PROBABILITY contents block. |
ICT practicals |
Practicals in the computer lab to introduce a statistical software helpful to solve problems.
|
Problem solving |
Seminars in small groups for the explanation and discussion of problems from the different contents blocks.
|
Guest lecture / keynote speech |
Face to face keynote speeches, where the lecturer will show the fundamental parts of the theoretical program, suitably illustrated with practical examples.
|
Supervised projects |
Practical project of analysis of a real data set with statistical software |
Objective test |
Final exam, with short answer questions and/or reasoned solution of practical problems, of the DESCRIPTIVE STATISTICS and STATISTICAL INFERENCE theoretical and practical content blocks.
|
Personalized attention |
Methodologies
|
ICT practicals |
|
Description |
Optionally, some academic work consisting on the solution of a practical problem using the statistical software introduced in the ICT practicals, could be carried out.
There will be personalized advice sessions during the development of the practical works. These sessions will take place by means of the interaction teacher/students at the moment of solving the different activities suggested in class: solving doubts, correcting mistakes, suggesting proper approaches to deal with the proposed problems and reviewing initial versions of the works. In addition, students will have the opportunity of receiving personalized advice face-to-face. Personalized advice may be also received via online, by means of e-mail, virtual platform,...
Part-time students may also perform these works and submit them to the teachers for their assessment. Part-time students can also receive personalized assistance using both face-to-face and virtual approaches. |
|
Assessment |
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
Short answer questions |
A21 B2 B3 B6 |
Achievement test to assess the progress in the PROB and ED-INF blocks. |
10 |
Objective test |
A26 A30 B2 B3 B10 |
Final test to assess the knowledge in the PROB block and DS-INF blocks.
|
70 |
Supervised projects |
A26 |
Practical project in groups to analyze real data set with statistical software |
20 |
|
Assessment comments |
The subject is split in two blocks: 1- Probability Theory (PROB) and 2-Descriptive Statistics-Statistical Inference (DS-INF). Each block will be assessed independently, so that passing one block will not affect the grade of the other. The score of block PROB is 45% (40% the objective test + 5% short answer questions), and the score of block DS-INF is 55% (supervised project for the DS part 10% + supervised projec for the INF part 10% + objective test 30% + short answer questions 5%). To pass the whole subject, it will be strictly necessary to pass each block separately. Otherwise, the final score will be 4.5 at most: (a) PROBABILITY: to pass this block the final score (objective test + short answer questions) must be 4.5 out of 10 at least. Besides, the score of the objective test must be 4 out of 10 at least. (a) ED-INF: to pass this block the final score (objective test + short answer questions + supervised projects) must be 4.5 out of 10 at least. Besides, the score of the objective test must be 4 out of 10 at least. During the course, one exemption exam for the PROB block might be performed, so that the student who passes the PROB block with this exemption exam, will have the corresponding block passed regarding the May/July final calls. To get the grade/mark NP (No grade reported, absent) in the first call (May), the student should not have attended any exemption exams nor the official test, nor submitted any supervised project. To get the grade/mark NP in the second call (July), the student should not attend any final exam. The attendance to the seminars, practical sessions, personalized attention, etc. and involvement along with the performance in the self-evaluation test (not evaluable) might be additionally scored with one extra point at most to be added to the final mark. For the second chance in July, the criterion to pass the subject will be the same as in the first call in June. All previous observations are applicable to part-time students and/or with academic exemption. Fraud
in tests or evaluation activities will
directly involve the implementation of the current rules in the Assessment, review and complaint regulation of the UDC and the Student Statute of the UDC
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Sources of information |
Basic
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• ARRIAZA GÓMEZ, A.J. (2008) Estadística básica con R y R-Commander . Servicio PublicacionesUCA. Available at http://sestio.uca.es/repos/ebrcmdr/pdf/13marzo/ebrcmdr.pdf • BEHAR GUTIÉRREZ, R. y GRIMA CINTAS, P. (2010). 55 respuestas a dudas típicas de estadística. 2a Ed. Díaz de Santos, Madrid. • CAMPOS ARANDA, M. (2011). Más de 777 preguntas de Bioestadística y sus respuestas . Murcia, DM. • CAO ABAD, R. y otros (2001). Introducción a la estadística y sus aplicaciones. Ed. Pirámide. • DE LA HORRA NAVARRO, J. (2001). Estadística Aplicada. 2ª Edición. Díaz de Santos. • GONICK, L. Y SMITH, W. (2001). A estatística ¡en caricaturas!. SGAPEIO. • MARTÍN, A. A. Y LUNA, J. C. (1999). Bioestadística para las Ciencias de la Salud. 4ª Edición revisada. Ediciones Norma. • MILTON, J. S. (2001). Estadística para Biología y Ciencias de la Salud.3ª edición. McGraw-Hill. • RIUS DÍAZ, F. y otros. (1999). Bioestadística: Métodos y Aplicaciones. Universidad de Málaga. • SAMUELS, M. L.; WITMER, J.A. Y SCHAFFNER, A. (2012). Fundamentos de estadística para las ciencias de lavida. 4ª edición. Pearson España • TOMEO PERUCHA V. y UÑA JUÁREZ I. (2003). Lecciones de Estadística Descriptiva. Paraninfo. • RIUS DÍAZ, F. y BARÓN LÓPEZ, F.J. (2005). Bioestadística. Thomson. |
Complementary
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• BARÓ LLINAS, J. (1988). Estadística Descriptiva, Cálculo de probabilidades e Inferencia estadística (tres volúmenes). Ed. Parramón. • CANAVOS, G.C. (1989). Probabilidad y Estadística. Aplicaciones y métodos. MacGraw-Hill. • CUADRAS, C.M. y otros (1989). Ejercicios de Bioestadística. Editorial Universitaria de Barcelona. • HERNÁNDEZ, V. RAMOS, E. y YÁNEZ, I. (1995). Estadística I. ITIS. UNED. • DANIEL, W. W. (1991). Biostatistics. A Foundation for Analysis in the Health Sciences. J. Wiley. •FISHER, L.D. Y VAN BELL, G. (1993). Biostatistics. A Methodology for the Health Sciences. John Wiley & Sons. • JOHNSON, R. A. Y BAHTTACHARIYA, G. K. (1992). Statistical Principes and Methods. J. Wiley. • MANN, P. S. (1995). Introductory Statistics. J. Wiley & Sons, INC. • NAVIDI, W. (2006). Estadística para ingenieros y científicos. 1ª Edición, Mc Graw-Hill. • PAGANO, M. Y GAUVREAU, K. (2001). Fundamentos de Bioestadística. 2ª Edición. Math Learning. • PEÑA SÁNCHEZ DE RIVERA, D. (1991). Estadística. Modelos y Métodos, 1. Fundamentos. Alianza Universidad. • QUESADA, V., ISIDORO, A. Y LÓPEZ, L. J. (1984). Curso y Ejercicios de Estadística. Alhambra Universidad. • ROSNER, B. (1990). Fundamentals of Biostatistics. PWS-KENT Publishing Company. • SOKAL, R.R. Y ROHLF, F.J. (1995). Biometry. The Principles and Practice of Statistics in Biological Research. 3ª Edición. W. H. Freeman and Company. • VIEDMA, J. A. (1976). Bioestadística (Métodos Estadísticos Aplicados a la Biología y Medicina). Ed. del autor. • ZAR, J.H. (1996). Biostatistical Analysis. Prentice Hall International Editions. ONLINE BOOKS• BARÓN LÓPEZ, F.J. Bioestadística. https://www.bioestadistica.uma.es/baron/apuntes/clase/apuntes/pdf/bioestadistica-libro.pdf • SÁEZ CASTILLO, A.J. (2010). Métodos estadísticos con R y R Commander. https://cran.r-project.org/doc/contrib/Saez-Castillo-RRCmdrv21.pdf • SEEFELD, K. Y LINDER, E. (2007). Statistics Using R with Biological Examples. https://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf BLOGS
• https://365datascience.com/explainer-videos/#statistics • https://estadisticaorquestainstrumento.wordpress.com/ • https://www.cienciasinseso.com/estadistica/ • https://www.fisterra.com/formacion/metodologia-investigacion/ DATA SET RESOURSES
• https://vincentarelbundock.github.io/Rdatasets/datasets.html • https://stats.idre.ucla.edu/other/dae/ • http://www.statsci.org/data/first.html |
Recommendations |
Subjects that it is recommended to have taken before |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
Data Analysis in Biology/610G02044 |
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Other comments |
Highly recommended: 1- Attendance and participation in the keynote sessions, practicals and seminars. 2- To solve every explained exercise, both with and without the statistical software. 3- To supplement the course material with the sources of information. 4- To study the course material and to solve the proposed problems frequently. 5- Active involvement in the practicals and seminars. 6- To become familiar with the statistical software by using it constantly and regularly.
7- To try to use the statistical techniques in other different subjects. 8- Attendance to and taking advantage of the personalized attention sessions. |
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