Checking date: 28/06/2021

Course: 2021/2022

Statistical methods for telecommunications
Study: Bachelor in Telematics Engineering (215)

Coordinating teacher: GARCIA PORTUGUES, EDUARDO

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Statistics Calculus I and II
* General skills - Ability to apply knowledge of mathematics, statistics, computer science, and engineering as it applies to the fields of computer hardware and software. - Ability to interpret data and results of experiments. - Ability to independently acquire and apply required information related to statistical techniques with the aim of designing, monitoring, and managing computer systems. - An ability to communicate effectively by oral, written, and graphical means, the results of statistical analysis. * Specific skills - An ability to identify statistical problems of multivariate dimension, with special emphasis in telecommunication engineering. - An ability to describe multivariate datasets. - Knowledge of multivariate statistical models. - An ability to solve statistical models for regression analysis, and ANOVA models, applied to real data of telecommunication engineering. - An ability to model time series data, estimate their parameters and apply it to real problems of signal processing and telecommunications.
Skills and learning outcomes
Description of contents: programme
1. Review of basic concepts 1.1. Descriptive Statistics 1.2. Probability 1.3. Random variables 1.4. Probability models 2. Point estimation 2.1. Introduction to statistical inference: population and sample 2.2. Sample statistics and their distribution 2.3. Estimation and estimators 2.4. Methods of maximum likelihood 3. Confidence intervals and hypothesis testing 3.1. Confidence intervals 3.2. Parametric hypothesis tests 4. Comparison of populations 4.1. Comparison of two means from independent samples 4.2. Comparison of two means from paired samples 4.3. Comparison of two proportions 5. The linear regression model 5.1. The simple regression model 5.2. The multiple regression model 5.3. Inference in the regression model
Learning activities and methodology
The learning methodology consists on the following elements: - Lecture classes: Presentation of the main concepts, with their justification and examples. The instructor will illustrate the methodologies with the computer and real or simulated data. Discussion of the concepts with the students. Discussion of the questions and doubts aroused during the self-learning process. - Exercises classes: Classes devoted to solving exercises in small groups. - Lab classes: In a computer room, the students, in small groups, solve data analysis problems using a statistical package. Also, students use the computer to solve exercises and conceptual problems.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Montgomery, D. C. and Runger, G. C.. Applied Statistics and Probability for Engineers. Wiley. 2007
  • Peña, D.. Fundamentos de Estadística. Alianza. 2001

The course syllabus may change due academic events or other reasons.