Course: 2019/2020

Statistical methods for telecommunications

(18480)

Students are expected to have completed

Statistics
Matlab

Competences and skills that will be acquired and learning results. Further information on this link

General competences:
-Ability to apply knowledge of mathematics, statistics, computer science, and engineering as it applies to the fields of computer hardware and software. (PO a)
-Ability to interpret data and results of experiments. (PO b)
-Ability to independently acquire and apply required information related to statistical techniques with the aim of designing, monitoring, and managing computer systems. (PO i)
-An ability to communicate effectively by oral, written, and graphical means, the results of statistical analysis. (PO g)
Specific competences:
-An ability to identify statistical problems of multivariate dimension, with special emphasys in telecommunication engineering.
-An ability to describe multivariate datasets.
-Knowledge of multivariate statistical models.
-An ability of 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.

Description of contents: programme

Chapter 1. Review of basic concepts
1.1 Descriptive Statistics
1.2 Probability
1.3 Random variables
1.4 Probability models
Chapter 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
Chapter 3.Confidence intervals and hypothesis testing
3.1 Confidence intervals
3.2 Parametric hypothesis tests
Chapter 4. Comparison of populations
5.1 Comparison of two means from independent samples
5.2 Comparison of two means from paired samples
5.3 Comparison of two proportions
Chapter 5. The linear regression model
6.1 The simple regression model
6.2 The multiple regression model
6.3 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. (PO i y g)
-Exercises classes: Classes devoted to solving exercises in small groups. (PO a y b)
-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. (PO a, b, i y g)

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