IDENTIFYING DATA 2023_24
Subject (*) STATISTICS Code 17234011
Study programme
Bachelor's Degree in Computer engineering (2010)
 Cycle 1st
Descriptors Credits Type Year Period
6 Basic Course Second 1Q
Language
Català
Department Computer Engineering and Mathematics
Coordinator
OLIVÉ FARRÉ, MARIA DEL CARME
BARBERÀ ESCOÍ, CARLOS
 E-mail carme.olive@urv.cat
carlos.barbera@urv.cat
jordi.soria@urv.cat
Lecturers
OLIVÉ FARRÉ, MARIA DEL CARME
BARBERÀ ESCOÍ, CARLOS
SORIA COMAS, JORGE
Web http://https://campusvirtual.urv.cat/local/alternatelogin/index.php
General description and relevant information <p>GENERAL DESCRIPTION OF THE SUBJECT: In this subject we intend to give a basic course in statistics, delving into the most applied techniques in computing. </p>

Competences
Type A Code Competences Specific
 A2 Have knowledge of taking measurements, calculations, evaluations, valuations, surveys, studies, reports, work plans and other similar studies in IT.
 FB1 Be able to solve mathematical problems that may arise in engineering. Have the ability to apply knowledge on: linear algebra, differential and integral calculation, numerical methods, numerical algorithmics, statistics and optimisation.
Type B Code Competences Transversal
 B2 Have knowledge in basic and technological subjects, which gives them the ability to learn new methods and theories, and the versatility to adapt to new situations.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A2 Calculate the descriptive statistical parameters of a population.
Use the most common probability distribution models to model real situations.
Know the situations modelled by stochastic processes.
Be able to analyse a situation from the point of view of statistical inference.
Understand the descriptive statistical parameters of a population.
 FB1 Understand binomial, normal, exponential and Poisson probability distributions.
Use the most common probability distribution models to model real situations.
Understand the fundamentals of queuing theory.
Be able to apply the fundamentals of queuing theory to IT.
Understand the basis of statistical inference.
Type B Code Learning outcomes
 B2 Master the central limit theorem.
Use the stochastic process techniques in specific problems.
Understand the fundamentals of queuing theory.
Know the techniques of regression.
Type C Code Learning outcomes

Contents
Topic Sub-topic
Descriptive statistics Data types, data representation graphs and measures of centralization and dispersion.
Probability Distributions Random experiments, sample space and probability. More common discrete models and continuous models. Central limit theorem.
Introduction to the estimation theory Point estimation and interval estimation.
Introduction to Hypothesis Testing Parametric Hypothesis Tests.
Goodness of fit Hypothesis Tests.
Stochastic processes Markov chains. Transition matrix and stationary probabilities.
Queue Theory Study of the fundamental models of queues. Computer applications.
Linear regression Relationship between two variables. Statistical evaluation of the goodness of fit

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
A2
 1 0 1
Lecture
FB1
 23 46 69
IT-based practicals in computer rooms
A2
FB1
B2
 13 15 28
Problem solving, exercises in the classroom
A2
FB1
B2
 13 22 35
Personal attention
FB1
 3 0 3
 
Practical tests
A2
FB1
B2
 1 7 8
Short-answer objective tests
A2
FB1
B2
 6 0 6
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities The subject will be presented and the development of the class sessions will be described, as well as the evaluation of the subject.
The need for statistical knowledge in computer science will also be motivated.
Lecture Transfer of the basic theoretical knowledge of the subject. The students will be able to take notes of the explanations made on the blackboard, through slides and/or video cannon.
IT-based practicals in computer rooms Formulation, analysis, resolution and discussion of problems or exercises related to the theme of the subject.
Problem solving, exercises in the classroom Problems will be solved in class with the active participation of the students.
Personal attention The teaching staff remains available to students who require personalized attention.
The schedule will be published in the virtual space of the subject.
You can also contact by email.

Personalized attention
Description

The teaching staff remains available to students who require personalized attention.

The schedule will be published in the virtual space of the subject.

You can also contact by email.

Assessment
Methodologies Competences Description Weight        
Practical tests
A2
FB1
B2
 The work carried out in the laboratory is evaluated. Also, the methodology developed and conclusions of an individual practice carried out with the software indicated at the beginning of the course are evaluated. 25%
Short-answer objective tests
A2
FB1
B2
 Partial individual tests through short questions with multiple choice solutions and/or development problems on the contents provided. There will be three tests during the course with differentiated weight in the final grade
 75%
Others  
 
Other comments and second exam session

The written test of the second call includes all the content of the subject.

The final grade for the course will be the highest grade between the two following options:

- 100% mark of the written test of the second call 

- 75% mark of the written test of the second call and 25% mark of the practical tests done in the current course.

In all evaluation tests, the use or possession of communication and data transmission devices is totally prohibited and will be mandatory for the students.

Sources of information

Basic J.L.Devore, Probabilidad y Estadística, Thomson, 2005
Larry Gonick y Woollcott Smith, La Estadística en cómic, Zendrera Zariquiey, 2ed. 2002
V.Zaiats, M.L.Calle i R. Presas, Probabilitat i Estadística. Exercicis I, Eumo, 1998
V.Zaiats, M.L.Calle, Probabilitat i Estadística. Exercicis II, Bellaterra : Universitat Autònoma de Barcelona, 2001

Complementary C.H.Brase and C.P.Brase, Understanding Basics Statistics, Houghton Mifflin, 2004
Spiegel, Schiller i Srinivasan, Probabilidad y Estadística, McGraw-Hill, 2000
Lapin, Probability and Statistics for Modern Engineering, PWS-KENT, 1990
Quintín Martín, Investigación Operativa, Pearson Prentice Hall, 2003

Recommendations


Subjects that it is recommended to have taken before
MATHEMATICAL ANALYSIS I/17234005
(*)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.