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Statistics II

Module name (EN):
Name of module in study programme. It should be precise and clear.
Statistics II
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Mechatronics, Master, ASPO 01.04.2020
Module code: MTM.STA
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P231-0113
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
1V+1U (2 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
3
Semester: according to optional course list
Mandatory course: no
Language of instruction:
German
Assessment:
Written exam and mini-project

[updated 01.10.2020]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

E2935 (P231-0113) Electrical Engineering and Information Technology, Master, ASPO 01.04.2019 , optional course, technical
E938 (P211-0263) Electrical Engineering, Master, ASPO 01.10.2005 , semester 9, optional course
E1922 (P211-0263) Electrical Engineering, Master, ASPO 01.10.2013 , optional course, technical
MTM.STA (P231-0113) Mechatronics, Master, ASPO 01.04.2020 , optional course, technical
MST.STA (P231-0113) Mechatronics and Sensor Technology, Master, ASPO 01.04.2016 , optional course, technical
MST.STA (P231-0113) Mechatronics and Sensor Technology, Master, ASPO 01.10.2011 , optional course, technical
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
30 class hours (= 22.5 clock hours) over a 15-week period.
The total student study time is 90 hours (equivalent to 3 ECTS credits).
There are therefore 67.5 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Gerald Kroisandt
Lecturer: Prof. Dr. Gerald Kroisandt

[updated 30.01.2019]
Learning outcomes:
Statistical methods play an important role in engineering-related studies, especially in electrical engineering, e.g. in analyzing stochastic signals and processes, planning experiments and evaluating observed data, modeling, simulating and optimizing processes, recognizing and modeling interrelationships.
Based on the basic course "Probability Calculation" (Higher Mathematics II (Part: Statistics) (E806), students in this course will learn advanced statistics methods. Mini-projects will help students learn to plan and implement the solution of more complex problems with more extensive data material using a statistics programming language (e.g. R).
After attending the lecture, they will be able to solve more complex statistical problems, as they occur in communications engineering and automation engineering, independently and in cooperation with mathematicians.

[updated 01.10.2020]
Module content:
1. Statistical inference methods
  1.1 Hypothesis testing
  1.2 Distribution tests
2. Generating random numbers
3. Stochastic processes
  (Definition, classification, covariance function and spectral density,      
  cross correlation function, stationarity, ergodicity)
4. Markov chains and applications in coding and information theory
5. Poisson point process
6. Markov processes
7. Birth and death processes
8. Introduction to traffic theory (= queueing theory)
9. Introduction to the simulation of discrete systems
10. Mini-projects
11. Stochastic signals
 
Depending on the clientele further/other topics:
 
12. Introduction to further statistical methods
   12.1 Regression and correlation analysis
   12.2 Analysis of variance
   12.3 Mini-projects

[updated 01.10.2020]
Teaching methods/Media:
Blackboard, overhead projector, video projector, lecture notes, PC

[updated 13.03.2010]
Recommended or required reading:
B.Grabowski: _ ActiveMath:Statistik: Statistik für Ingenieure technischer Fachrichtungen an Fachhochschulen - e-Lerning-Buch_,
H.Weber: _Einführung in die Wahrscheinlichkeitsrechnung und Statistik für Ingenieure_
B.Grabowski:_ Lexikon der Statistik_, Elsevier-Verlag, 2001
B.Grabowski:_ Stochastik_, Lehrmaterial für das Fernstudium, Zentralstelle für Fernstudien an Fachhochschulen , ZFH Koblenz, 2004.
B.Grabowski:_ Die Simulationssprache AWESIM_, Lehrmaterial für das Fernstudium, Zentralstelle für Fernstudien an Fachhochschulen , ZFH Koblenz, 2000.
B.Grabowski:_ Mathematische Methoden bei der Simulation diskreter Systeme_,
Lehrmaterial für das Fernstudium, Zentralstelle für Fernstudien an Fachhochschulen , ZFH Koblenz, 2000.
 
To be found at  www.htw-saarland.de/fb/gis/mathematik:
1) Vorlesungs-Skript I and II (Lecture notes I and II) - Internet
2) Formelsammlungen 1 und 2 zum Skript I und II (Formulas 1 and 2 for script I and II)
3) Übungsaufgaben und Lösungen zum Skript I und II (Exercises and solutions to script I and II)
4) Lernserver ACTIVEMATH

[updated 01.10.2020]
 
[Sun Dec 22 17:20:59 CET 2024, CKEY=esi, BKEY=mechm, CID=MTM.STA, LANGUAGE=en, DATE=22.12.2024]