| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Engineering | ||
| ELECTRONIC ENGINEERING | ||
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
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
Information on the course unit
| Degree course |
First cycle degree in ELECTRONIC ENGINEERING IN0507, Degree course structure A.Y. 2011/12, A.Y. 2017/18 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 9.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | STATISTICAL DATA ANALYSIS | |
| Department of reference | Department of Information Engineering | |
| Mandatory attendance | No | |
| Language of instruction | Italian | |
| Branch | PADOVA | |
| Single Course unit | The Course unit can be attended under the option Single Course unit attendance | |
| Optional Course unit | The Course unit can be chosen as Optional Course unit |
Lecturers
| Teacher in charge | LORENZO VANGELISTA | ING-INF/03 |
|---|
Mutuating
| Course unit code | Course unit name | Teacher in charge | Degree course code |
|---|---|---|---|
| INL1000178 | STATISTICAL DATA ANALYSIS (Ult. numero di matricola da 0 a 4) | LORENZO VANGELISTA | IN0513 |
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 |
| Start of activities | 26/02/2018 |
|---|---|
| End of activities | 01/06/2018 |
| Show course schedule | 2019/20 Reg.2011 course timetable |
Examination board
| Board | From | To | Members of the board |
|---|---|---|---|
| 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: | Analisi matematica1, Analisi Matematica 2, Algebra lineare e geometria. |
| Target skills and knowledge: | 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: | Witten exam |
| Assessment criteria: | 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: | 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: | 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: | 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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