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Statistics (EST)

Credits Dept. Type Requirements
9.0 (7.2 ECTS) EIO
  • Compulsory for DIE
  • Compulsory for DCSFW
  • Compulsory for DCSYS
AL - Prerequisite for DIE , DCSYS , DCSFW
CAL - Prerequisite for DIE , DCSYS , DCSFW


Person in charge:  (-)

General goals

This subject introduces techniques for data analysis (descriptive statistics...), which allow students to take on the challenge of interpreting the sorts of data they will encounter in the professional world. The subject also introduces basic aspects of applied probability, which computer scientists may need over the course of their professional careers. These basics lay the foundations for working in the following areas: system and/or software reliability, the study of system performance, the processing of simulation and queue theory data, the design of quantitative information systems and the statistical exploitation of databases, et cetera.

Specific goals




    population parameter, sample estimate, interval estimate, hypothesis testing, model, decision, risk.
  3. Examples of applications of which the foregoing concepts, ranging from games of chance to situations commonly found in computing.


  1. Knowing how to apply mathematical formalism to solve problems involving uncertainty.
  2. Learn how to distinguish stochastic from deterministic components, and understand the need to quantify the degree of uncertainty.
  3. Knowing how to use statistical software, a scientific calculator and/or tables to calculate the most important probability distributions.
  4. Knowing how to use statistical software to analyze data and obtain numerical indication and graphs summarising the most relevant information.
  5. Knowing how to turn data into information that is of use for taking decisions.


  1. Critical, logical-mathematical reasoning.
  2. Ability to design and carry out experiments and analyse the results.
  3. Ability to apply a realistic model to a non-deterministic situation, estimate the parameters involved, study the model"s validity, and use the model to make forecasts or take decisions after giving due consideration to the sampling error.
  4. Ability to take take decisions when faced with uncertainty, assuming quantified risks in doing so.
  5. Preparation for group work.


Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

1. Descriptive statistics
T      P      L      Alt    Ext. L Stu    A. time Total 
0 0 8,0 0 8,0 0 0 16,0
  • Laboratory
    4 weeks x 2 hours/week.

2. Probability
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 2,0 0 0 0 6,0 0 12,0

3. Variable random
T      P      L      Alt    Ext. L Stu    A. time Total 
14,0 4,0 0 0 0 22,0 0 40,0
Probability distribution models.

4. Comparison of systems using sampling systems
T      P      L      Alt    Ext. L Stu    A. time Total 
12,0 4,0 4,0 0 4,0 16,0 0 40,0
Sampling, statistical inference, estimation, testing hypotheses.

5. Applied statistical methods
T      P      L      Alt    Ext. L Stu    A. time Total 
10,0 6,0 6,0 0 6,0 16,0 2,0 46,0
Linear model, ANOVA.

6. Introduction to the modelling of systems
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 0 4,0 0 4,0 4,0 0 16,0
Introduction to simulation. Applications to queuing models.

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
44,0 16,0 22,0 0 22,0 64,0 2,0 170,0
Avaluation additional hours 10,0
Total work hours for student 180,0

Docent Methodolgy

Lectures illustrated with slides and whiteboard notes. These methods may be replaced with a computer and LCD projector, facilitating the use of ICT (e.g. software demonstrations, the use of applets, Internet access, etc.).

Students will tackle problems on a co-operative basis. For example, a group of students will work on a problem for which the preparatory work has been done by other groups. Exercises will also be carried out on computers providing self-correction in a so-called e-status environment. The teacher may occasionally explain and solve exercises for illustration purposes.

In the lab, students will work in groups with their respective computers, following work guidelines covering the session in question.

The teacher will be on hand to provide guidance and to answer questions.

Students will read the guidelines before each session. Students may also be required to finish off work after the session and complete a questionnaire.

Evaluation Methodgy

The final grade will be made up as follows:
- (20%) a partial grade corresponding to some problems or multiple-choice test during the term and held in class time (between three and five)
- (20%) a lab grade corresponding to a maximum of six weigthed tests carried out during the term
- (60%) a final exam

If the final exam grade is higher than the grade obtained using the previous weigths, the course grade awarded will be that obtained in the final exam.

Basic Bibliography

  • William A. Florac, Anita D. Carleton. Measuring the software process : statistical process control for software process improvement, Addison-Wesley, 1999.
  • José Javier Dolado Cosín, Luis Fernández Sanz Medición para la gestión en la ingeniería del software, RA-MA, 2000.
  • Raj Jain The Art of computer systems performance analysis : techniques for experimental design, measurement, simulation, and modeling, John Wiley & Sons, 1991.
  • Daniel Peña Sánchez de Rivera Estadística : modelos y métodos, Alianza, 1986-1991.
  • Thomas H. Wonnacott, Ronald J. Wonnacott Introducción a la estadística, Limusa, 1999.

Complementary Bibliography

  • Arnold O. Allen Probability, statistics and queueing theory : with computer science applications, Academic Press, 1990.
  • Stephen H. Kan Metrics and models in software quality engineering, Addison-Wesley, 2003.
  • Larry Gonick, Woollcott Smith La Estadística en cómic, Zendrera Zariquiey, 2002.

Web links


Previous capacities

Students must have sufficient knowledge of algebra and mathematical analysis in order to assimilate concepts regarding set algebra, numerical series, the functions of real variables in one or more dimensions, derivation and integration.


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