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

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

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

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	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.

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

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