Identifying Data 2018/19
Subject (*) Statistical Methods for Environmental Data Code 610500006
Study programme
Mestrado Universitario en Ciencias. Tecnoloxías e Xestión Ambiental (plan 2012)
Descriptors Cycle Period Year Type Credits
Official Master's Degree 1st four-month period
First Optional 3
Language
Spanish
Galician
English
Teaching method Face-to-face
Prerequisites
Department Matemáticas
Química
Coordinador
Jacome Pumar, Maria Amalia
E-mail
maria.amalia.jacome@udc.es
Lecturers
Andrade Garda, Jose Manuel
Estevez Perez, Maria Graciela
Jacome Pumar, Maria Amalia
E-mail
jose.manuel.andrade@udc.es
graciela.estevez.perez@udc.es
maria.amalia.jacome@udc.es
Web
General description Nos estudos medioambientais manéxanse xeralmente importantes cantidades de datos, cuio análise permitirá a extracción da información relevante contida neles. Nesta materia farase unha introdución ás técnicas estatísticas avanzadas necesarias para a análise multivariable de datos, que permiten a reducción da dimensionalidade e a construción de grupos dende un punto de vista descritivo. O desarrollo dos ordenadores facilita o procesamento de grandes bancos de datos, resultando polo tanto unha materia de moito interese práctico.
Contingency plan

Study programme competencies
Code Study programme competences
A1 Coñecemento das realidades interdisciplinares da Química e do Medio Ambiente, dos temas punteiros nestas disciplinas e das perspectivas de futuro.
A3 Capacitar ao alumno para o desenvolvemento dun traballo de investigación nun campo da Química ou do Medio Ambiente, incluíndo os procesos de caracterización de materiais, o estudo das súas propiedades fisicoquímicas e biolóxicas e dos procesos que poden sufrir no medio natural.
A12 Coñecer as distintas estratexias para o tratamento estatístico de series de datos relacionadas con datos ambientais.
B3 Que os estudantes sexan capaces de integrar coñecementos e enfrontarse á complexidade de formular xuízos a partir dunha información que, sendo incompleta ou limitada, inclúa reflexións sobre as responsabilidades sociais e éticas vinculadas á aplicación dos seus coñecementos e suizos.
B5 Que os estudantes posúan as habilidades de aprendizaxe que lles permitan continuar estudando dun modo que haberá de ser en gran medida autodirixido ou autónomo.
B6 Ser capaz de analizar datos e situacións, xestionar a información dispoñible e sintetizala, todo iso a un nivel especializado.
C1 Ser capaz de traballar en equipos, especialmente nos interdisciplinares e internacionais.
C3 Ser capaz de adaptarse a situacións novas, mostrando creatividade, iniciativa, espírito emprendedor e capacidade de liderado.
C6 Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida.
C9 Valorar criticamente o coñecemento, a tecnoloxía e a información dispoñible para resolver os problemas cos que deben enfrontarse.
C10 Asumir como profesional e cidadán a importancia da aprendizaxe ao longo da vida.

Learning aims
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

Contents
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

Planning
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
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.

Personalized attention
Methodologies
Problem solving
Description
Students will be required to develop a study on a particular dataset. They will apply the different techniques learnt in this subject, along with a critical discussion of the results and addressing several predefined questions. They will be monitored by the teachers so that they can solve their doubts with both "face-to-face" and online advice sessions.

Tutorships will take place at the office of the teachers for solving doubts, correcting mistakes, suggesting proper approaches to deal with the proposed problems and reviewing initial versions of the works. Online advice sessions will be by means of e-mail, virtual platform, and similar.

Part-time students may also perform these works and provide them to the teachers for their assessment. Part-time students can also receive personalized assistance using both in-person and online approaches.

Assessment
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.


Sources of information
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


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

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.



(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.