The contains of this course aim to train the future engineer in the basics of data analysis, mainly in the analysis of data coming from different sources, as the engineer surely will work with professionals from other areas in interdisciplinary teams.





The ultimate goal of all this is to obtain information on process results, estimating future behaviours and planning experiments. In addition, it is intended to provide the students with a scientific basis that, together with intellectual attitudes and the necessary resources, allows them to understand with their own effort, on the one hand, other subjects in which Statistics is a tool and, on the other,to learn and apply new techniques in new situations. All of which will help them adapt to the labour market successfully.

* Learning of the basics of probability theory and its application to statistical inference.



* Building of statistical models that respond to real problems.



* Using graphical and numerical methods for data exploration, synthesis and description and understanding of the results, all those with the help of statistical software.

1. Introduction to Statistics and Probability.



2. Descriptive statistics.

2.1. Frequency distributions and Graphic representations.

2.2. Central tendency measures, Dispersion and Position measures.

2.3. Bivariate distributions.



3. Probability calculus.

3.1. Basic theory of Probability.

3.2. Random variables and probability distributions.

3.3. Sampling, simulation and central limit theorems.



4. Statistical Inference

4.1. Estimation.

4.2. Hypothesis testing.

4.3. Distribution fitting.





NOTE: The programming language R will be used to work the theoretical concepts seen in class.

Different methodologies are used in this subject, including the flipping classroom methodology.



Sometimes the student will be asked to elaborate some concepts before the teacher's explanantion, then solving the doubts and openning a debate that will reinforce the student's autonomy. In the master classes brief explanantions will be given on the conceptual contents of the subject, combining them with the examples to be completed by the student.



In the classroom students will participate in the resolution of doubts and problems. They will be given problems and exercises to be performed individually

or in collaboration. This will allow to deepen the theoretical aspect of the subject.



In the laboratory sessions, theoretical concepts will be reinforced and their applications will be displayed.



Students must attend 60 hours of lectures and dedicate 90 hours to homework assignments.

• Continuous Assessment System
• Final Assessment System
• Tools and qualification percentages:
  • Types of evaluation and percentages are explained below (%): 100

There are two ways of grading the subject: continuous assessment or final assessment (global assessment). The continuous assessment system is the one that will be used preferentially, as indicated in the current UPV/EHU regulations.





CONTINUOUS ASSESSMENT:

- Written tasks associated with the continuous assessment (tests, exams, homework ) - 85%

A minimum mark (3/10) will be required in each written test a for continuous assessment. In addition, by the time that 55% of the subject have been assessed, the student must have obtained at least half of the points assessed to follow the continuous assessment, otherwise the final assessment will be applied.



- Exercises done in the laboratory - 15%

In the total of written tests a minimum grade (4/8.5) will be required so that the practical part is assessed.



In the event that a student who fulfils the requirements for continuous assessment wishes to opt for the final assessment, he/she must inform the teachers responsible for the subject in writing. This information must be given during the week following the moment in which 55% of the subject is assessed.



The final grade will be the average of partial grades and the final grade to pass the subject must be at least 5.







GLOBAL OR FINAL ASSESSMENT:

- Written exams in the scheduled/published data for the ordinary and extraordinary calls- 85%

- Exercises done in the laboratory - 15%

A minimum grade (4.5/10) in the written exam will be required for the assessment of the practical part.

In the event that the written exam is not carried out, it is understood that the student waives the call.

It is similar to the final assessment in the ordinary call.

- Written exams in the scheduled/published data for the ordinary and extraordinary calls- 85%

- Laboratory exercises - 15%



In the event that the written exam is not carried out, it is understood that the student waives the call. A minimum grade (4/8.5) in the written exam will be required for the assessment of the practical part.



In the event that the written exam is not carried out, it is understood that the student waives the call.

The final grade will be the average of partial grades and the final grade to pass the subject must be at least 5.

The programming language R and the RStudio environment.

Basic bibliography

Tomeo & alt - Lecciones de Estadística descriptiva - Thomson - 2003.

Tomeo & alt - Lecciones de Cálculo de Probabilidades - Thomson - 2003.

Canavos - Probabilidad y Estadística. Aplicaciones y métodos - McGraw-Hill - 1988.

Peña - Fundamentos de Estadística - Alianza editorial - 2001.

Milton - Probabilidad y estadística con aplicaciones para ingeniería y ciencias computacionales - 4a ed. - Mcgraw-Hill - 2004.

Trivedi - Probability and Statistics with Reliability, Queuing, and Computer Science Applications - Wiley - 2001.

Agresti & Franklin - Statistics: the art and science of learning from data - 2nd ed. - Pearson. Prentice Hall - 2009.

Montgomery - Probabilidad y estadística aplicadas a la ingeniería - 2a ed. - Limusa Wiley - 2004.

Navidi - Estadística para ingenieros - Mcgraw-Hill / Interamericana - 2006.

Mendenhall - Probabilidad y estadística para ingeniería y ciencias - Prentice Hall, 1997.

Devore - Probabilidad y Estadística para Ingeniería y Ciencias. International Thomson, 2001.

Walpole & alt.- Probabilidad y Estadística para Ingenieros. Prentice Hall Hispanoamericana, 1999.

In-depth bibliography

Peña - Estadística. Modelos y métodos - Alianza Universidad - 1991.
Paradis - R for beginners http://www.r-project.org - 2006.
Pérez - Estadística aplicada a través de Excel - Prentice Hall - 1992.

Web addresses

http://onlinestatbook.com/rvls.html
http://www.economics.pomona.edu/StatSite/framepg.html
http://www.mathcs.carleton.edu/probweb/probweb.html
http://www.statsci.org
http://estadistico.com

http://www.r-project.org
http://ocw.uc3m.es/estadistica/aprendizaje-del-software-estadistico-r-un-entorno-para-simulacion-y-computacion-estadistica