Statistical Methods for Telecommunications
Universidad Carlos III de Madrid
Area of Study
Electrical Engineering, Systems Engineering
Taught In English
STUDENTS ARE EXPECTED TO HAVE COMPLETED:
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Recommended U.S. Semester Credits3
Recommended U.S. Quarter Units4
Hours & Credits
Statistical methods for telecommunications (217 - 15942)
Study: Bachelor in Communication System Engineering
Semester 2/Spring Semester
3rd Year Course/Upper Division
Students are Expected to have completed:
Compentences and Skills that will be Acquired and Learning Results:
-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)
-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: Course Description
Chapter 1. Review of basic concepts
1.1 Descriptive Statistics
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 moments and of maximum likelihood
Chapter 3.Confidence intervals and hypothesis testing
3.1 Confidence intervals
3.2 Parametric hypothesis tests
Chapter 4. Distribution fitting
4.1 Graphical methods
4.2 Goodness of fit chi-square test
Chapter 5. 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 variances from normal populations
Chapter 6. The multiple regression model
6.1 The simple regression model
6.2 The multiple regression model
6.3 Inference in the regression model
Chapter 7. Time series analysis
7.1 Introduction to time series analysis
Chapter 8. Aplications in telecommunication engineering
8.1 Inference problem solving in the telecommunicacion field
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)
The evaluation of the course will be based on continuous evaluation by means of written assignments with MATLAB and partial exams of theoretical and practical contents (PO a, b, i y g).
The continuous evaluation mark will be calculated giving a 75% weight to the weighted average mark of the partial exams and a 25% weight to the average mark of the written assignments with MATLAB (PO b, d, e, g).
If the continuous evaluation mark is not below 5, the student does not need to take the final exam and his/her final mark will be equal to his/her continuous evaluation mark.
If the continuous evaluation mark is below 5, the student must take a final exam that will consist of theoretical and practical problem solving.
Final exam - ordinary session
The student's final mark will be calculated giving a 70% weight to the continuous evaluation mark, and a 30% weight to the final exam.
Final exam - extraordinary session
The evaluation system in the extraordinary session will be the higher of the following two criteria:
a) 100% of the final exam
b) 70% of the continuous evaluation mark + 30% of the final exam
Peña, D.. Fundamentos de Estadística. Alianza. 2001
Courses and course hours of instruction are subject to change.
ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.