Ongoing monitoring of attendance and ongoing assessment of knowledge acquisition by checking lists of solved problems and the learning level shown during the seminars.

The official exams of both opportunities (January and July) consist in answering a list of short and conceptual questions about the application and interpretation of the studied statistical methods.

Requirements to pass the subject are: (i) passing the official exam and (ii) performing one or two practical works where the studied statistical techniques will be used to deal with specific practical problems provided by the professors. Scores attained with these works are valid throughout the course and these requirements hold for both opportunities (January and July). If the practical works are not carried out in January, they must be performed in July. This also applies to part-time students.

The final score could be increased up to 1 point (considering a total maximum score of 10 points) according to the results of the ongoing assessment for the student.

If the practical works are not presented in due course and the official exams are not carried out, then the specific mark "NON PRESENTADO" will be given.

All previous observations are applicable to part-time students and/or with academic exemption.

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
Simple regression models	Simple linear regression model Other regression models
Design and analysis of experiments	Basic principles. Planning experiments Basic designs 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	23.8	37.8
Problem solving	A26 B2 B3 B5 B10	5	9	14
Guest lecture / keynote speech	A26 B2 B3 B6 B10	24	55.2	79.2
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.
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.	50
Objective test	A21 A26 A30 B2 B3 B4 B6 B10	Test for assessment of knowledge.	50

Assessment comments
Ongoing monitoring of attendance and ongoing assessment of knowledge acquisition by checking lists of solved problems and the learning level shown during the seminars. The official exams of both opportunities (January and July) consist in answering a list of short and conceptual questions about the application and interpretation of the studied statistical methods. Requirements to pass the subject are: (i) passing the official exam and (ii) performing one or two practical works where the studied statistical techniques will be used to deal with specific practical problems provided by the professors. Scores attained with these works are valid throughout the course and these requirements hold for both opportunities (January and July). If the practical works are not carried out in January, they must be performed in July. This also applies to part-time students. The final score could be increased up to 1 point (considering a total maximum score of 10 points) according to the results of the ongoing assessment for the student. If the practical works are not presented in due course and the official exams are not carried out, then the specific mark "NON PRESENTADO" will be given. All previous observations are applicable to part-time students and/or with academic exemption.

Basic
	· Kuehl, R.O. (2001) Diseño de Experimentos.Principios estadísticos para el diseño y análisis de investigaciones. 2nded. Thomson Learning. · 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.
Complementary
	·  Box, G.E.P., Hunter, W.G. & Hunter, J.S. (1978). Statistics for Experimenters. An introduction to Design, Data Analysis, and Model Building. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, Inc.  ·  Cao,R. et al. (2001). Introducción a la Estadística y sus aplicaciones. Ed. Pirámide, Madrid.  ·  Dean, A. & Voss, D.  (1999) Design and Analysis of Experiments. Springer-Verlag, New York.  ·  Gibbons, J.D. & Chakraborti, S. (1992). Nonparametric Statistical Inference. 3rd ed. Marcel Dekker, New York (1992).  ·  Jobson, J.D. (1992). Applied Multivariate Analysis. Vol. II: Categorical and Multivariate Methods.  Springer Texts in Statistics, Springer-Verlag: New York.  ·  Martín Andrés, A. & De Dios Luna del Castillo, J. (1994). Bioestadística para las Ciencias de la Salud. 4ª Edición. Eds. NORMA S.A.  ·  Millard, S.P. & Neerchal, N.J. (2001) Environmental Statistics with S-Plus. Springer. CRC Press LLC.  ·  Prat, A., Tort-Martorell, X., Groma, P. & Pozueta, L. (1997). M’etodos estadísticos. Control y mejora de la calidad. Edicions UPC (Universitat Politécnica de Catalunya).  ·  Zar, J.H. (1996). Biostatiscal Analysis. 3rd. ed. Prentice Hall International.