TecnoCampus Foundation
eCampus
University Business
eCampus

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General information

Subject type: Basic

Coordinator: Vladimir Bellavista Parent

Trimester: Second term

Credits: 6

Teaching staff: 

Moses Burset Albareda

Academic year: 2025

Teaching course: 2

Languages ​​of instruction

  • Catalan

Competencies / Learning Outcomes

Basic skills

  • B1_That students have demonstrated knowledge and understanding in a field of study that is based on general secondary education, and is accustomed to finding at a level that, although with the support of advanced textbooks, also include some aspects that involve knowledge from the forefront of your field of study

  • B3_Students have the ability to gather and interpret relevant data (usually within their area of ​​study), to make judgments that include reflection on relevant social, scientific or ethical issues

  • B4_That students can convey information, ideas, problems and solutions to both specialized and non-specialized audiences

Specific skills

  • EFB3_Ability to understand and master the basic concepts of discrete mathematics, logic, algorithms and computational complexity, and their application for solving engineering problems

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 the available resources

Presentation of the subject

The understanding and ability to analyze random phenomena can be of great relevance in some branches of computer engineering, such as in the processing and analysis of biological information (bioinformatics). There are processes that, by their very nature, are random (such as the study of the time that can pass until a machine breaks down, how big an animal will be, how many users will use a certain program, ...) the which, paradoxically, does not imply that they are not treatable and/or modelable phenomena.

Any form of academic fraud will be sanctioned in accordance with the center's assessment regulations. If signs of fraud are detected, including the improper use of generative artificial intelligence tools, the subject's teaching staff may call the student for an individual interview with the aim of verifying their authorship.

 

Contents

1.-Descriptive statistics
  1.1.-Concept of random variable (VA)
  1.2.-Types of variables (quantitative, qualitative)
  1.3.-Population and sample
  1.4.-Statistics concept (centrality, dispersion)
  1.5.-Concept of probability
  1.6.-Probability density function
  1.7.-Distribution function
  
2.-Probabilities
  2.1.-Probabilities as sets (Venn diagrams)
  2.2.-Intersection, union and conditional probabilities
  2.3.-Theorem of total probabilities
  2.4.-Bayes' theorem

3.-Distributions
  3.1.-Bernouilli
  3.2.-Binomial
  3.3.-Poisson
  3.4.-Exponential
  3.5.-Normal
  
4.-Inference
  4.1.-Contingency tables
  4.2.-Types of errors
  4.3.-Contrast of hypotheses (1 population)
  4.4.-Contrast of hypotheses (2 populations)
  4.5.-Analysis of variance (contrast of n populations)
  
5.-Regressions
  5.1.-Simple linear regression
  5.2.-Coefficients of the line
  5.3.-Quality of adjustment (r square)
  5.4.-Multiple linear regression
  5.5.-Logistic regression
 

Activities and evaluation system

P = partial exam // F = final exam

70% grade => maximum(0.35 P+0.35 F, 0.7 F)
15% grade => group work
15% mark => individual questions (oral exam type)

In the final exam you must obtain a minimum grade of 5 to pass the subject. If you do not reach this mark, you will have to go to the recovery exam.

The grade of the group work and the individual grade are not recoverable.

The maximum mark in the recovery exam will be 8.

The minimum overall grade to pass the subject will be 5.

Normative: Any form of academic fraud will be sanctioned in accordance with the center's assessment regulations. If signs of fraud are detected, including the improper use of generative artificial intelligence tools, the subject's teaching staff may call the student for an individual interview with the aim of verifying their authorship.

Bibliography

Basic

Course notes (available on the virtual campus)

Devore, Jay L. Probability and statistics for engineering and science. (9th edition). Ed. Cengage. 2016

Pena, Daniel Fundamentals of statistics. Editorial Alliance (2014).

Complementary

Montgomery, Douglas C; Runger, George C. Applied statistics and probability for engineers (3rd edition). Ed. John Wiley and sons. 2003. (in English)