Attendance to the guest lectures and active participation will be scored positively (up to 10% of the final overall score of the subject). Attendance should not be lower than 80% of the total hours of the subject (but for clearly justified reasons). The remaining 90% of the overall score will be obtained by performing a written report on a practical case-study. This task may be supervised by the teachers so that main doubts are solved. Scoring of the reports will consider: formal aspects, clarity in the written explanations, sound defence/basement of the explanations and, when required, the performance on the oral presentation. All activities (problem solving, working team essays) posed by the teachers must be addressed by the students, otherwise the subject will not be passed. The overall final score will be a weighted sum of the scores obtained in the different parts.

For part-time students 100% of the overall score will be obtained by performing a written report on a practical case-study and they are not required to defend their works in class.

To obtain a NR (No Grade Reported), the student must not participate in the collaborative learning activities.

Learning outcomes	Study programme competences
Design experiments, get information and interpret results	AC3 AC12	BC3 BC6	CC1 CC6 CC9 CC10
Apply critical, logical and creative thinking to solve problems as effectively as possible.	AC1 AC3	BC5	CC3

Topic	Sub-topic
Introduction	A review of the basic methods to describe a dataset, univariate and multivariate approaches.
Relationships among variables	Dependence measurements: correlation matrix, simple and multiple linear regression; multicolinearity.
Multivariate analysis	Description of multivariate datasets Principal components analysis Discriminant analysis Cluster analysis

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Problem solving	A1 A3 A12 B3 C3 C1 C6 C10	5	15	20
Collaborative learning	A3 A12	0	6	6
Guest lecture / keynote speech	A12 B5 B6 C6 C9 C10	16	32	48

Personalized attention		1	0	1

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies	Description
Problem solving	After finishing the theoretical classes, practical exercises will be reviewed in the classroom, and might be proposed as autonomous work.
Collaborative learning	Collaborative learning groups work, consisting on applying the concepts to a real dataset dealing with environmental issues. This training example may be reviewed in the classroom.
Guest lecture / keynote speech	Theoretical lessons will be devoted to teach the basic concepts involved in the selected data treatment techniques, along with practical examples of each of them.

Methodologies	Competencies	Description	Qualification
Guest lecture / keynote speech	A12 B5 B6 C6 C9 C10	Attendance to the theoretical classes and participation there will be scored positively.	5
Problem solving	A1 A3 A12 B3 C3 C1 C6 C10	Participation in the class, in particular, to address the resolution of the exercises will be scored positively.	5
Collaborative learning	A3 A12	Students will analyze a dataset and they will present their findings in a written report. The study may be individual or forming small working teams	90

Assessment comments
Attendance to the guest lectures and active participation will be scored positively (up to 10% of the final overall score of the subject). Attendance should not be lower than 80% of the total hours of the subject (but for clearly justified reasons). The remaining 90% of the overall score will be obtained by performing a written report on a practical case-study. This task may be supervised by the teachers so that main doubts are solved. Scoring of the reports will consider: formal aspects, clarity in the written explanations, sound defence/basement of the explanations and, when required, the performance on the oral presentation. All activities (problem solving, working team essays) posed by the teachers must be addressed by the students, otherwise the subject will not be passed. The overall final score will be a weighted sum of the scores obtained in the different parts. For part-time students 100% of the overall score will be obtained by performing a written report on a practical case-study and they are not required to defend their works in class. To obtain a NR (No Grade Reported), the student must not participate in the collaborative learning activities.

Basic
	Jobson, J.D. (1992). Applied Multivariate Analysis. Vol. II: Categorical and Multivariate Methods. Springer Texts in Statistics, Springer-Verlag: NewYork. Miller, J.N. & Miller, J.C. (2002) Estadística y Quimiometría para Química Analítica. Edit. PrenticeHall. Mongay Fernández, C. (2005) Quimiometría. Servicio Publicaciones Universidad de Valencia. Morrison, D.F. (1990) Multivariate statistical method. 3rd Edition. McGraw-Hill Series in Probability and Statistics. Peña, D. (2002). Análisis de Datos Multivariantes. McGraw-Hill. Pérez López, C. (2004) Técnicas de análisis multivariante de datos. Aplicaciones con SPSS. Pearson Prentice Hall, Madrid. Pérez López, C. (2005) Métodos Estadísticos Avanzados con SPSS. Thomson, Madrid. Ramis Ramos, G. (2001) Quimiometría. Síntesis, Madrid.
Complementary
	Millard, S.P. & Neerchal, N.J. (2001) Environmental Statistics with S-Plus. Springer. CRC Press LLC

Other comments
Active participation in the classes is recommended. It is also important to combine the notes taken by the students with the books and reports suggested by the teachers. Tutorships are available for the students, specially for those whose basic skills in statistics may be faulty. It is recommended to review the notes of the subject daily.