IDENTIFYING DATA 2023_24
Subject (*) MATHEMATICS II Code 20204006
Study programme
Bachelor's Degree in Chemical Engineering (2010)
Cycle 1st
Descriptors Credits Type Year Period
6 Basic Course Second 1Q
Language
Català
Department Mechanical Engineering
Chemical Engineering
Coordinator
FERNÁNDEZ SABATER, ALBERTO
E-mail alberto.fernandez@urv.cat
roger.girbes@urv.cat
deepak.parajuli@urv.cat
Lecturers
FERNÁNDEZ SABATER, ALBERTO
GIRBES BALAGUE, ROGER
PARAJULI , DEEPAK
Web
General description and relevant information <div><p>GENERAL DESCRIPTION OF THE SUBJECT:</p><p>To know the statistical techniques to perform data analysis correctly and efficiently. To know how to apply basic mathematical tools to be able to solve problems in the area of engineering.</p></div>

Competences
Type A Code Competences Specific
 A1.1 Consistently apply knowledge of basic, scientific and technological subjects pertaining to engineering
 A3.1 Ability to solve a wide range of mathematical problems in engineering. Ability to apply the knowledge of linear algebra, geometry, differential geometry, differential and integral calculus, differential equations and partial differential equations, numerical methods, numerical algorithmics, statistics and optimisation (FB1)
Type B Code Competences Transversal
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A1.1 Aplica correctament els principis matemàtics que puguin plantejar-se en l’enginyeria, àlgebra lineal, geometria, geometria diferencial, càlcul diferencial i integral, equacions diferencials i en derivades parcials, mètodes numèrics, algorítmica numèrica, estadística i optimització.
 A3.1 Adquireix les tècniques més elementals del càlcul numèric i aplicar-les, amb l’ajuda d’un llenguatge de programació estructurat d’alt nivell a models matemàtics relacionats amb l’enginyeria.
Coneix els mecanismes estadísticament correctes per a un anàlisi eficient de dades: interpretació i de presa de decisions sobre els valors de paràmetres físics o químics.
Coneix els mètodes més usuals d’optimització i saber utilitzar-los en la resolució de problemes de l’àmbit de l’enginyeria.
Type B Code Learning outcomes
Type C Code Learning outcomes

Contents
Topic Sub-topic
1. Descriptive statistics. Mean, variance and standard deviation.
2. Probability distribution models: binomial, Poisson, normal.
3. Point estimation and confidence intervals estimation.
4. Hipothesis testing.
5. Analysis of variance.
6. Least-squares aproximation. Linear regression and multiple linear regression.
7. Introduction to optimization methods. Search of maxima and minima. Lagrange multipliers.
8. Introduction to ordinary differential equations (ODE). Analytical solutions for first and second degree linear ODEs.
9. Introduction to partial derivatives differential equations. Separable variables.
10. Introduction to differential geometry.

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
Hours outside the classroom
(**) Total hours
Introductory activities
1 1.5 2.5
Lecture
A1.1
A3.1
22 33 55
Problem solving, exercises in the classroom
A1.1
A3.1
14 21 35
IT-based practicals in computer rooms
A1.1
A3.1
14 21 35
Personal attention
A1.1
A3.1
1 1.5 2.5
 
Short-answer objective tests
A1.1
A3.1
2 8 10
Short-answer objective tests
A1.1
A3.1
2 8 10
 
(*) 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 Introduction of the subject where the lecturer discusses the content to be worked on, the objectives to be evaluated, the methodology to be used, and the evaluation system.
Lecture The lecturer explains the theoretical content of each topic.
Problem solving, exercises in the classroom The lecturer solves problems in classroom.
IT-based practicals in computer rooms Students are asked to do and deliver practicals, carried out with a computer, and related to the contents that are being worked on. These practicals are part of the continuous assessment of the subject.
Personal attention Students can receive personal attention in person or telematically during the hours of attention to students, and during practical hours in classroom.

Personalized attention
Description

Time that each lecturer has reserved to attend to and solve students' doubts.


Assessment
Methodologies Competences Description Weight        
IT-based practicals in computer rooms
A1.1
A3.1
Students will have to solve, with a computer, problems about various contents of the subject. The practical exercises will be assessed. 0-20%
Short-answer objective tests
A1.1
A3.1
Individual test of a synthesis character on the contents developed during the first part of the subject. 40-50%
Short-answer objective tests
A1.1
A3.1
Individual test of a synthesis character on the contents developed during the second part of the subject. 40-50%
Others  
 
Other comments and second exam session

Continuous assessment:

The practice grade will only be taken into account when it is higher than the average grade of the two partial tests. In this case, the weights of the practice grade and the two partial tests will be 20%, 40% and 40%, respectively. Otherwise, these weights will be 0%, 50% and 50%, respectively.

Second call:

The final grade will consist of 100% for the grade of an individual objective test on the content of the entire subject.


Sources of information

Basic Mateo, J.M., Estadística pràctica pas a pas, Universitat Rovira i Virgili,
Zill, D.G.; Wright, W.S., Matemáticas avanzadas para ingeniería, McGraw-Hill,

Complementary

Recommendations


Subjects that it is recommended to have taken before
MATHEMATICS I/20204005
(*)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.