General information


Subject type: Basic

Coordinator: Julián Horrillo Tello

Trimester: Third term

Credits: 6

Teaching staff: 

Cristina Steegmann Pascual

Skills


Basic skills
  • B5_That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy

     

Specific skills
  • E1_Training for the resolution of mathematical problems that may arise in engineering. Train to apply knowledge about: linear algebra; geometry; differential geometry; differential and integral calculus; differential equations and partial derivatives; numerical methods; numerical algorithm; statistics and optimization

Transversal competences
  • T2_That students have the ability to work as members of an interdisciplinary team either as one more member, or performing management tasks, in order to contribute to developing projects with pragmatism and a sense of responsibility, making commitments taking into account available resources

     

Description


The subject as a discipline of science responsible for learning from data and analyzing phenomena with uncertainty provides the basis for: synthesizing information, analyzing random phenomena with the application of probability theory and the study of different probability distributions. Applied examples of sampling and statistical inference applied in areas close to the degree areas and an introduction to linear models will be given.

 

Learning outcomes


At a general level, this subject contributes to the following learning outcomes specified for the subject to which it belongs (Mathematics)

  • Use the fundamental concepts of non-deterministic analysis and statistics in engineering problems
  • Analyze and critique the results of engineering problems (Statistical view)

At a more specific level, at the end of the course the student must be able to:

Ra1: Describe the general structure of a data analysis project and the use of linear models.

Ra2: Use descriptive statistics to synthesize information, both from a graphical and numerical perspective

Ra3: Know and be able to apply the main properties of Probability Theory to solve problems.

Ra4: Solve problems of random phenomena with the identification of the reference distribution.

Ra5: Identify, use and represent the Gaussian distribution.

Ra6: Apply concepts of simulation, sampling and linear models in problem solving.

Ra7: Solve statistical inference problems using confidence intervals and hypothesis tests.

 

Working methodology


All the theoretical concepts of the subject will be exposed in theory classes (large groups) and / or in laboratory sessions (small groups). In these classes, and at the discretion of the teachers, exercises and problems of a more practical nature will also be solved. Likewise and always at the discretion of the teachers, students may be asked to solve, individually or in groups, short problems and / or exercises. These activities, which due to their optional nature and brevity, will serve the student as an instrument of self-assessment of their achievement of the contents of the subject and can be used by the teacher to assess it.

 The more practical concepts and everything that can essentially be considered the practical application of the theoretical concepts will be worked on in small (laboratory) groups. In the sessions scheduled for this purpose, the appropriate tools will be given to solve the scheduled activities. Sometimes students will have to complete them during the time of autonomous learning. Whenever deemed appropriate, totally optional activities will be made available to students to help them prepare and prepare for the compulsory ones.

 

Contents


1.- Descriptive Statistics

2.- Probability

3.- Discrete Random Variable

4.- Continuous Random Variable

5.- Statistical Inference

Learning activities


A series of activities of an eminently practical nature (short exercises, problems ...) are made available to students, which are the basis of the learning activities of the subject. These activities will have to be solved by the students, often in a non-contact way, following the instructions of the teachers and will also be worked in class, either as examples in the theory sessions or in the laboratory sessions. Although these activities will be optional (teachers will not individually verify the performance by students), they will be essential to achieve the theoretical and practical knowledge of the subject.

In order to gather evidence of the achievement of the expected learning outcomes, the following evaluative activities are carried out:

PT = Written test (Exam) [Related to competences E1 and B5]

    The test will include the contents associated with the following learning outcomes: Ra2, Ra3, Ra4, Ra5 and Ra7

PLab = Laboratory Practices [Related to all competencies]

    The practices will allow the student to understand a problem / s that involves a data analysis project as well as its solution using data analysis software.

Evidence of learning outcomes: Ra1, Ra2, Ra5, Ra6 and Ra7

Proj = Data Analysis Project Presentation [Related to all competencies]

    Students will present a data analysis problem describing each and every one of the stages they have developed. The code, the working document and the presentation will be delivered

Evidence of learning outcomes: Ra1, Ra2, Ra6 and Ra7

PP = Practical Part with practical exercises in exam [Related to competences E1 and T2]

    Students involved in problem solving

Evidence of learning outcomes: Ra2, Ra3, Ra4, Ra5 and Ra7

Remarks:

To pass the assessment activities, students must demonstrate the MECES Level - 2:

• (point c) have the ability to collect and interpret data and information on which to base their conclusions, including, where necessary and relevant, reflection on issues of a social, scientific or ethical nature in the field of their field of study

• (point e) know how to communicate to all types of audiences (specialized or not) in a clear and precise way, knowledge, methodologies, ideas, problems and solutions in the field of their field of study;

• (point f) be able to identify their own training needs in their field of study and work or professional environment and to organize their own learning with a high degree of autonomy in all types of contexts

For each activity, teachers will be informed of the particular rules and conditions that govern them

The one-to-one activities presuppose the student's commitment to carry them out individually and without any collaboration with other people. All activities in which the student does not comply with this commitment to individuality will be considered suspended (grade 0), regardless of their role (sender or receiver) and without this excluding the possible application of other sanctions in accordance. with the current Disciplinary Regime.

Likewise, the activities to be carried out in groups presuppose the commitment on the part of the students who make it up to carry them out within the group and without any kind of collaboration with other groups or people who are alien (group individuality). All activities in which the group has not respected this commitment regardless of its role (sender or receiver) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime will be considered suspended (rating 0).

In the case of activities that can be done in groups, when in any of them the commitment of group individuality is not respected and / or fraudulent means are used in its accomplishment, the qualification of the activity will be, for all members of the group, of 0 points (Activity Note = 0) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime.

Any undelivered activity will be considered scored with zero points

It is optional for teachers to accept or not deliveries outside the deadlines indicated. In the event that these late deliveries are accepted, it is up to the teacher to decide whether to apply a penalty and the amount of this.

 

Evaluation system


The final grade is the weighted sum of the grades for the learning activities:

Q = 0.80 (PT + PP) + 0.10 PLab + 0.10 Proj

The theory part of the subject (PT) + the practice part (PP) must be completed and a minimum of 5 points must be taken in order to be able to choose to count the other scores.

Remarks on Recovery

The theory part of the subject (PT) + practical part (PP) is indeed recoverable. The rest of the parts are not recoverable. For students who attend the make-up exam, their grade will be the one obtained in this test and their final grade (Q) will be calculated using the formulas detailed above and in no case will it be higher than 7.

 

REFERENCES


Basic

Sanchís, C .; Salillas, J .; Riera, T .; Fontanet, G. (1987): Making statistics. Madrid (Spain), Alhambra

MENDENHALL, William and SINCICH, Terry. Statistics for Engineering and the Sciences. 5. Prentice Hall, 2006.

Complementary

 Max Kuhn and Kjell Johnson, Applied Predictive Modeling. Sringer 2013