Course: 2022/2023

Statistical methods for telecommunications

(18480)

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
1.5. Fit of distributions
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. Method 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
4.4. Comparison of two variances in normal populations
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 of the following elements:
- Lecture lessons: 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 lessons: lessons devoted to solving exercises in small groups.
- Laboratories: 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

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.