Identifying Data 2019/20
Subject (*) Statistical data analysis Code 730495005
Study programme
Mestrado Universitario en Materiais Complexos: Análise Térmica e Reoloxía (plan 2012)
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
 First Obligatory 3
Language
English
Teaching method Face-to-face
Prerequisites
Department Matemáticas
Coordinador
Naya Fernandez, Salvador
 E-mail
salvador.naya@udc.es
Lecturers
Francisco Fernandez, Mario
Naya Fernandez, Salvador
 E-mail
mario.francisco@udc.es
salvador.naya@udc.es
Web http://www.udc.es
General description Trátase de proporcionar aos estudantes con habilidades de procesamento dos datos estatísticos, modelos de regresión, métodos numéricos.

Study programme competencies
Code Study programme competences
A4 Knowing and applying statistical methods to analyze data from complex material testing
B2 The students have the skill to apply their knowledge and their ability to solve problems in new or unfamiliar contexts within broader (or multidisciplinary) contexts related to their field of study
B3 That students are able to integrate knowledge and handle complexity, and formulate judgments from an information that, being limited or not complete, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments
B4 That the students can communicate their conclusions and the knowledge and last reasons behind that conclusions to specialized and non specialized audience in a clear and unambiguous way
B6 Learning to learn
B7 Solving problems effectively
B9 To work autonomously with initiative
B12 Communicate effectively in the work environment
B13 Analysis-oriented attitude
B18 Ability for abstraction, understanding and simplification of complex problems
C2 Have a good command of spoken and writing expression and understanding of a foreign language.
C4 Developing for the exercise of an open, educated, critical, committed, democratic and solidary citicenship, able to analyze reality, diagnose problems, formulate and implement solutions based on knowledge and oriented to the common good.
C6 Critically assessing the knowledge, technology and information available to solve the problems they face with.
C7 To assume as a professional and citizen the importance of learning throughout life.
C8 To assess the importance of research, innovation and technological development in the socio-economic and cultural progress of society.

Learning aims
Learning outcomes Study programme competences
To train students in theoretical and methodological principles for quantitative research, in the sense of design of experiments and regression models BR7
Know the most common statistical techniques in the field of thermal analysis and rheology BR2
BR6
BR7
Knowing and applying statistical methods to analyze data from complex material testing AR4
The students have the skill to apply their knowledge and their ability to solve problems in new or unfamiliar contexts within broader (or multidisciplinary) contexts related to their field of study BR2
That students are able to integrate knowledge and handle complexity, and formulate judgments from an information that, being limited or not complete, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments BR3
That the students can communicate their conclusions and the knowledge and last reasons behind that conclusions to specialized and non specialized audience in a clear and unambiguous way BR4
Learning to learn BR6
Solving problems effectively BR7
To work autonomously with initiative BR9
Communicate effectively in the work environment BR12
Analysis-oriented attitude BR13
Ability for abstraction, understanding and simplification of complex problems BR18
Have a good command of spoken and writing expression and understanding of a foreign language. CR2
Developing for the exercise of an open, educated, critical, committed, democratic and solidary citicenship, able to analyze reality, diagnose problems, formulate and implement solutions based on knowledge and oriented to the common good. CR4
Critically assessing the knowledge, technology and information available to solve the problems they face with. CR6
To assume as a professional and citizen the importance of learning throughout life. CR7
To assess the importance of research, innovation and technological development in the socio-economic and cultural progress of society. CR8

Contents
Topic Sub-topic
The following blocks or topics develop the contents established in the Verification Report, which are: Design of Experiments (Basic Principles, ANOVA model, factorial designs, repeated measurements designs, RyR laboratory design)
Regression Analysis (Simple linear regression, general linear regression: multiple regression, diagnosis of atypical or influential observations, construction of a regression model, nonlinear regression). Applications in thermal analysis and rheology data
I. Exploratory Data Analysis 1.1. Introduction to statistical analysis
1.2. Frequency distributions.
1.3. Graphical plots.
1.4. Characteristic measures: Measures of location, variability and shape.
1.5. Vectors of variables.
1.6. Frequency distribution of bivariate vectors.
1.7. Graphical plots of bivariate vectors.
1.8. Characteristic measures of bivariate vectors.
II. Statistical inference 2.1. Introduction.
2.2. Point estimation.
2.3. Confidence Intervals.
2.4. Hypothesis testing.
III. Regression Models 3.1. Introduction.
3.2. Simple linear regression models.
3.3. Parameter estimation by least squares.
3.4. Properties of the estimators.
3.5. Inference for the parameters.
3.6. Validation of a regression model.
3.7. Correlation.
3.8. Other regression models.
IV. Design and Analysis of Experiments 4.1. Basic principles of design of experiments.
4.2 Planning stages of an experiment.
4.3. Designs with a source of variation. The ANOVA model.
4.4. Designs with several factors. Factorial designs.
4.5. Factorial designs and response surfaces.
4.6. Experimental designs applications to complex materials.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A4 B2 B3 B4 B6 10 13 23
Supervised projects C2 C4 C6 C7 C8 5 20 25
ICT practicals B7 B12 B13 2 12 14
Objective test A4 B2 B9 B18 2 8 10
 
Personalized attention 3 0 3
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Guest lecture / keynote speech Students will receive lectures where the professor, with the help of relevant audiovisual media, will present the theoretical and practical contents of the subject. Participation and debate will be encouraged at all times.
Supervised projects Methodology designed to promote independent learning of students under the tutelage of a Professor and in various settings (academic and professional). It refers primarily to learning "how to do things."
ICT practicals Methodology that allows students to effectively learn through practical activities (proofs, simulations, data analysis using statistical packages, etc.) the theory of a field of knowledge, using information technology and communications . ICT brings excellent support and a channel for information processing and practical application of knowledge, facilitating learning and skills development by students.
Objective test Multiple choice test of basic issues matter.

Personalized attention
Methodologies
Guest lecture / keynote speech
Supervised projects
Description
Resolution of doubts, clarifications, etc.

Analysis and critical evaluation of scientific literature.

Help your approach and follow up.

Personal monitoring of each stage of the course work set (individual or group).

Accompanying the students with explanations.

Assessment
Methodologies Competencies Description Qualification
Guest lecture / keynote speech A4 B2 B3 B4 B6 Theoretical explanation of nuclear issues or basic notions of the subject. Attendance by students at these sessions is mandatory and it compute in the final grade.

For enrolled part-time students, this percentage of the mark may be less than 20%. 		20
Supervised projects C2 C4 C6 C7 C8 Methodology designed to promote independent learning and in group of students, based on the assumption by the students of responsibility for their own learning under the tutelage of Professor in various settings (academic and professional). It refers primarily to the learning of "how to do things".
 20
ICT practicals B7 B12 B13 Included the presentations that students do of the various mentored works. It deals with fundamental questions of the subject using ICT, particularly the use of statistical programs for data processing. Through a small group or individual tutoring, the teacher will guide the process of carrying out the work as non-presential methodology, based on the practices performed during the course. 20
Objective test A4 B2 B9 B18 Examination of the concepts covered in the course. 40
 
Assessment comments

The presentation by the student of the course work posed in the
subject must be done at least on the official date of the examination of
the subject for each one of the calls the student attends.

The evaluation system in the case of academic exemption will be the same as the one described in this section.

The criteria for evaluating the second opportunity are the same as those for the first opportunity

Sources of information
Basic http://www.r-project.org/ (). .
Peña, D. (2002). Regresión y diseño de experimentos. . Alianza Editoria
Gareth J., Witten, D., Hastie, T. and Tibshirani R. (2013). An Introduction to Statistical Learning. Springer
Vikneswaran (2005). An R companion to “Experimental Design”. URL http://CRAN.R-project.org/doc/contrib/Vikneswaran-ED-companion.pdf.
Draper, N.R. y Smith, H. (1998). Applied Regression Analysis.. Wiley. Greene, W.
Cao R., Franciso M, Naya S., Presedo M., Vázquez M., Vilar J.A. and Vilar J.M. (2001). Introducción a la Estadística y sus aplicaciones. . Editorial Pirámide
José Hernández Orallo, M.José Ramírez Quintana, Cèsar Ferri Ramírez. (2004). INTRODUCCIÓN A LA MINERÍA DE DATOS. Editorial Pearson.
Faraway, J.J. (2004). Linear models with R. . Chapman and Hall.
Venables, W.N. y Ripley, B.D. (2002). Modern applied statistics with S. . Springer
Ugarte L. Militino A. and Arnholt A. (2007). Probability and Statistics with R. CRC Press

 
Complementary Montgomery, D.C. (2009). Design and Analysis of Experiments. 7th Edition,. J. Wiley and Sons
Box, G.E.P., Hunter, W.G. y Hunter J.S. (2005). Statistics for Experimenters: Design, Innovation, and Discovery. 2nd. Edition, . Wiley, New York


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
To help achieve a sustained immediate environment and meet the objective of action number 5: "Healthy and sustainable environmental and social teaching and research" of the "Green Campus Ferrol Action Plan":
            The delivery of the documentary work carried out in this subject:
              - They will be requested in virtual format and/or computer support
              - It will be done through Moodle, in digital format without the need to print them.
             - If it is necessary to make them on paper:
                  - Plastics shall not be used.
                 - Double-sided printing shall be carried out.
                 - Recycled paper will be used.
                 - Printing of drafts shall be avoided.

A sustainable use of resources and the prevention of negative impacts on the natural environment must be made.


(*)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.