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Course unit
STATISTICAL DATA ANALYSIS (Ult. numero di matricola da 0 a 4)
INL1000178, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2015/16
ECTS: details
Type |
Scientific-Disciplinary Sector |
Credits allocated |
Core courses |
ING-INF/04 |
Automatics |
5.0 |
Core courses |
ING-INF/03 |
Telecommunications |
4.0 |
Course unit organization
Period |
Second semester |
Year |
3rd Year |
Teaching method |
frontal |
Type of hours |
Credits |
Teaching hours |
Hours of Individual study |
Shifts |
Lecture |
9.0 |
72 |
153.0 |
No turn |
Examination board
Board |
From |
To |
Members of the board |
24 A.A. 2019/2020 |
01/10/2019 |
15/03/2021 |
CALVAGNO
GIANCARLO
(Presidente)
VANGELISTA
LORENZO
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
BENVENUTO
NEVIO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
LAURENTI
NICOLA
(Supplente)
MILANI
SIMONE
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
ZANELLA
ANDREA
(Supplente)
ZANUTTIGH
PIETRO
(Supplente)
ZORZI
MICHELE
(Supplente)
|
23 A.A. 2018/2019 |
01/10/2018 |
15/03/2020 |
VANGELISTA
LORENZO
(Presidente)
FINESSO
LORENZO
(Membro Effettivo)
ZANELLA
ANDREA
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
CALVAGNO
GIANCARLO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
LAURENTI
NICOLA
(Supplente)
MILANI
SIMONE
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
ZANUTTIGH
PIETRO
(Supplente)
ZORZI
MICHELE
(Supplente)
|
22 A.A. 2018/2019 |
01/10/2018 |
15/03/2020 |
FINESSO
LORENZO
(Presidente)
VANGELISTA
LORENZO
(Membro Effettivo)
CALVAGNO
GIANCARLO
(Supplente)
|
21 A.A. 2017/2018 |
01/10/2017 |
15/03/2019 |
VANGELISTA
LORENZO
(Presidente)
FINESSO
LORENZO
(Membro Effettivo)
ZANELLA
ANDREA
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
CALVAGNO
GIANCARLO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
LAURENTI
NICOLA
(Supplente)
MILANI
SIMONE
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
ZANUTTIGH
PIETRO
(Supplente)
ZORZI
MICHELE
(Supplente)
|
20 A.A. 2017/2018 |
01/10/2017 |
15/03/2019 |
FINESSO
LORENZO
(Presidente)
VANGELISTA
LORENZO
(Membro Effettivo)
CALVAGNO
GIANCARLO
(Supplente)
|
19 A.A. 2016/2017 |
01/10/2016 |
15/03/2018 |
VANGELISTA
LORENZO
(Presidente)
FINESSO
LORENZO
(Membro Effettivo)
ZANELLA
ANDREA
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
BENVENUTO
NEVIO
(Supplente)
CALVAGNO
GIANCARLO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
MILANI
SIMONE
(Supplente)
PUPOLIN
SILVANO
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
ZANUTTIGH
PIETRO
(Supplente)
ZORZI
MICHELE
(Supplente)
|
12 A.A. 2016/2017 |
01/10/2016 |
15/03/2018 |
FINESSO
LORENZO
(Presidente)
VANGELISTA
LORENZO
(Membro Effettivo)
CALVAGNO
GIANCARLO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
TOMASIN
STEFANO
(Supplente)
ZANELLA
ANDREA
(Supplente)
|
Prerequisites:
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Analisi matematica1, Analisi Matematica 2, Algebra lineare e geometria. |
Target skills and knowledge:
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Basic knowledge of probability theory, random variables and random processes. At the end of the course the student should be able to build simple probability models of random phenomena and be ablle to make related probabilistic calculations |
Examination methods:
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Witten exam |
Assessment criteria:
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The students must demonstrate that they have acquired the basic knowledge of probability theory, discrete and continuous random variables and the fundamentals of random processes. Also they will have to demonstrate the ability to apply the theory to find appropriate probabilistic models related to random phenomena and to know how to solve problems of calculation of probabilities. |
Course unit contents:
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Probability:
Probability spaces and their properties. Elements of combinatorics and classic probability problems. Conditional probability. Independent events.
Random Variables (RV):
Definition of RV. Distribution function and its properties. RV continuous, discrete and mixed. Examples of fundamental discrete RV. RV absolutely continuous, probability density. Examples of fundamental RV continuous. Transformations of RV. Expected value, moments and their properties. Moment generating function and characteristic function. Gaussian RV. Markov and Chebychev inequalities.
Random Vectors (RVe):
Definition of Rve. Joint distribution and its properties. Continuous RVe. Joint density and its properties. Discrete RVe. Joint probability density and its properties. Expected value of RVe and moments RVe. Characteristic function of a RVe. Random variables uncorrelated and independent. Gaussian RVe. Sum of independent RV.
Sequences of Random Variables:
Sequences of RV. Convergence in distribution, in probability, on average. Law of large numbers and central limit theorem.
Random processes:
Definitions. Complete probabilistic description and power description. Stationariety. Correlation and spectral density. Spectral analysis of filtered random processes. |
Planned learning activities and teaching methods:
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Class lessons. During the lessons the theoretical aspects of the course are exposed and application examples and exercises are carried out. Additional individual exercises as homework with subsequent illustration of the solutions are proposed. |
Additional notes about suggested reading:
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All course topics discussed in the classroom. The lecture notes can be integrated from the textbook and additional material made available on the Moodle platform. |
Textbooks (and optional supplementary readings) |
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L.Finesso, Probabilità. Padova: Libreria Progetto, 2017.
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J.A. Gubner, Probability and Random Processes for Electrical and Computer Enguneering. Cambridge: Cambridge University Press, 2006.
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