Research in Engineering Education Symposium 2011 - rees2009

Universidad Politécnica de Madrid (UPM) Página 589 de 957
Introduction
Statistics has an important role in the curricula of Telecommunication Engineering
students, and its importance is twofold:
• The statistical tools has a direct application in Telecommunication Engineering
subjects, such as Detection Theory, Communication Theory, etc.
• All Telecommunication Engineering students have some subjects related to
general Enterprise management and technology management, and quantitative
(statistical) analysis tools are necessary.
However, the teaching of statistics in Telecommunication Engineering curricula has been
traditionally a very difficult challenge. Engineering students has a good preparation in
probability theory (the formal models for uncertainty before data are collected), but they
experience serious difficulty in dealing with statistical inference (that is to say, to extract
information about facts related to populations using a limited random sample of data from
this population). However, Statistics is a very important tool and should be clearly
understood and assimilated by Telecommunication Engineering students.
Structure of the Statistical Decision-Making Laboratory
The Statistical Decision-Making Laboratory is currently designed as a case-study
laboratory. It is divided into six modules, which are (Newbold, Carlson & Thorne, (2008);
Law & Kelton, (2000)):
1. Descriptive Statistics Module. In this module the concepts of data grouping,
frequency tables, graphs, central trend measures and dispersion measures are
introduced.
2. Time Series Analysis Module. In this module the students learn to construct and
interpret the additive models of time series (trend, seasonal, cyclic components
and residual component).
3. Estimation Theory Module. The main objective of this module is that the
students learn to obtain estimators of distribution parameters, and to associate
and interpret the corresponding confidence intervals of these estimators. The
adjustment of a candidate distribution function to a data set is also considered.
4. Decision Theory Module. This module considers the design of hypothesis tests,
for one parameter and two parameters.
5. Analysis of Variance Module. In this module the student designs one way and
two way ANOVA tests.
6. Simulation module. Finally, in this module, the main steps of the design of
complex system simulators are presented. The statistical analysis of performance
parameters obtained by simulation is also presented.
In all modules, the students must consider a set of synthetically generated data, and they
must apply the techniques presented in the theoretical sessions in order to obtain
quantitative measures to support a specific decision concerning a real problem based in a
case of study. They resume their solution and decision in a final report for each module.
Proceedings of Research in Engineering Education Symposium 2011
Madrid, 4 th - 7 th October 2011