Study programme competencies |
Code
|
Study programme competences
|
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. |
B4 |
Traballar de forma autónoma con iniciativa. |
B5 |
Traballar en colaboración. |
B6 |
Organizar e planificar o traballo. |
B10 |
Exercer a crítica científica. |
Learning aims |
Learning outcomes |
Study programme competences |
To learn how to design experiments, to acquire and develop skills to interpret and discuss statistical results. |
A21 A26 A30
|
B2 B3 B5 B6 B10
|
|
Developing critical and creative thinking skills to address problems in an effective way. |
|
B2 B3 B4 B5 B6 B10
|
|
Contents |
Topic |
Sub-topic |
Lineal regression models
|
Simple linear regression model
Multiple linear regression model
Other regression models |
Design and analysis of experiments
|
Basic principles. Planning experiments
ANOVA models with one and more than one sources of variation
Complete blocks designs
Designs including random effects
Introduction to covariance analysis
|
Introduction to multivariate analysis |
Description of multivariate data
Principal component analysis
Multivariate analysis of variance
Discriminant analysis
Cluster analysis |
Planning |
Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
Supervised projects |
A21 A26 A30 B2 B3 B4 B5 B6 B10 |
4 |
10 |
14 |
ICT practicals |
A26 A30 B2 B3 B10 |
14 |
22.4 |
36.4 |
Problem solving |
A26 B2 B3 B5 B10 |
6 |
9.6 |
15.6 |
Guest lecture / keynote speech |
A26 B2 B3 B6 B10 |
22 |
55 |
77 |
Short answer questions |
A21 B2 B3 B6 |
2 |
0 |
2 |
Objective test |
A21 A26 A30 B2 B3 B4 B6 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 |
Supervised projects |
Students should develop one or two practical works related to the subject contents. These works could be defended during a pre-established seminar.
|
ICT practicals |
Practical classes in the computer lab conducted to provide some knowledge on the use of statistical software (mainly the R-commander package). These classes are specifically designed to learn the elementary use of the package and to interpret its outputs. Use of software helps to focus attention on the statistical issues rather than on the calculation.
|
Problem solving |
Solving real problems in order to use statistical techniques fluently, empashizing their practical application. |
Guest lecture / keynote speech |
Lectures where the basic theoretical principles of the subject are presented together with properly illustrated practical examples. |
Short answer questions |
Multiple choice and short answer questions to assess the progress for each unit of the subject. They will be online using moodle.udc.es. |
Objective test |
Final exam on the theoretical and practical contents of the subject. This exam consists in answering a list of short questions and/or solving some longer exercises in a reasoned way. |
Personalized attention |
Methodologies
|
Supervised projects |
|
Description |
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 to this, students will have the opportunity of receiving personalized advice in the office of the teachers. Personalize advice may be also received via online (e-mail, virtual platform,...).
Part-time students are not required to defend their works in class, but these works must be provided 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 |
Description
|
Qualification
|
Supervised projects |
A21 A26 A30 B2 B3 B4 B5 B6 B10 |
Application of several statistical techniques to practical cases. |
40 |
Objective test |
A21 A26 A30 B2 B3 B4 B6 B10 |
Test for skills assessment. |
40 |
Short answer questions |
A21 B2 B3 B6 |
Multiple choice and short answer questions to assess the progress for each unit of the subject. They will be online using moodle.udc.es. |
20 |
|
Assessment comments |
The
objective tests, in each of the two opportunities, will consist of multiple
choice and short answer questions, related to the application of the studied statistical
methodologies and the interpretation of the corresponding results. The
supervised projects will be practical projects in group, with the
implementation of some of the different statistical methodologies to a real
data set given by the teacher/s, using statistical software (R). The score of
the supervised projects will be kept during the current academic course. In
case one (or both) supervised project(s) is (are) not submitted for the first
opportunity in January, it (they) may be submitted for the second opportunity
in July. To pass the subject, it will be
strictly necessary to pass each block separately. Otherwise, if only one or no
blocks are passed, the final score will be 4.5 at most. To pass each block, it
is necessary that the score of the objective test, for this block, is not lower than 3 out of
10 and the global score of all the assessment activities of the block is not lower than 4.5 out of 10. For any of the two opportunities to pass the subject,
the “NON PRESENTADO” grade will be given only to the students who did not
submit any of the supervised projects nor take the objective test. All these remarks are applied to the
part-time students and/or with academic exemption. All these remarks are applied to the December session exam. 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
|
Sources of information |
Basic
|
|
Basic: - Materials developed by professors of subject. They will be available in Campus Virtual.
Complementary:- Kuehl, R.O. (2001) Diseño de Experimentos.Principios estadísticos para el diseño y análisis de investigaciones. 2nded. Thomson Learning.
- Logan, M. (2011). Biostatistical design and analysis using R: a practical guide . John Wiley & Sons.
- Mangiafico S (2019). rcompanion: Functions tosupport extension education program evaluation. R package version 2 (https://rcompanion.org/rcompanion/index.html)
- McDonald JH (2014). Handbook of biological statistics. 3rd ed Sparky House Publishing, Baltimore, USA. (http://www.biostathandbook.com/small.html)
- Milton, J.S. (2001). Estadística para Biología y Ciencias de la Salud , 3ª Edición,McGraw-Hill.
- Montgomery, D.C. (2005) Design and Analysis of Experiments. 6thEdtition J. Wiley and Sons.
- Peña, D. (2002). Análisis de DatosMultivariantes . McGraw-Hill.
- Peña, D. (2002). Regresión y diseño de experimentos. Alianza Editorial
- Sarabia Alegría, J. M., Prieto Mendoza, F., & Jordá Gil, V. (2018). Prácticas de estadística con R. Comercial Grupo ANAYA, SA.
- Valiente, L. P., & Tejedor, I. H. (2014). Bioestadística sin dificultades matemáticas. Ediciones Díaz de Santos.
|
Complementary
|
|
|
Recommendations |
Subjects that it is recommended to have taken before |
|
Subjects that are recommended to be taken simultaneously |
|
Subjects that continue the syllabus |
|
Other comments |
1- Attendance and participation in both theoretical and practical lectures.
2- Complete all the problems solved in the development of the classes, with and without using statistical software. 3- Complement the materials provided by the teachers using the recommended references.
4- Continually review the work done in class by solving questionnaires and proposed problems. 5- Active participation in seminars scheduled for the presentation and defense of practical works. 6- Regular use of statistical software. 7- Application of statistical techniques to address problems arising in other subjects.
8- Take advantage of a regular participation in the personalized tutorial sessions. Green Campus Science Faculty Program To contribute to achieve an immediate sustainable environment and comply with point 6 of the "Environmental Declaration of the Faculty of Sciences (2020)", the documentary works carried out in this subject: - They will be requested mostly in virtual format and electronic form. - If it is printed: - Plastics will not be used. - Double-sided prints will be made. - Recycled paper will be used. - Drafts will be avoided.
|
|