## EXPERIMENTAL DATA PROCESSING ELABORAZIONE DEI DATI SPERIMENTALI

A.Y. Credits
2023/2024 9
Lecturer Email Office hours for students
Gianluca Maria Guidi Friday, 4 pm- 6 pm.
Teaching in foreign languages
Course with optional materials in a foreign language English
This course is entirely taught in Italian. Study materials can be provided in the foreign language and the final exam can be taken in the foreign language.

### Assigned to the Degree Course

Applied Informatics (LM-18)
Curriculum: PERCORSO COMUNE
Date Time Classroom / Location
Date Time Classroom / Location

### Learning Objectives

The course aims to introduce the basic logical and conceptual methodologies to lead learners to a correct approach to the problems of data analysis.

The objectives concern a correct use of formalization and analysis procedures in the application of probability theory and statistics.

### Program

Fundamental concepts

1.1 Probability and random variables

1.2 Interpretation of probability

1.3 Probability density functions

1.4 Functions of random variables

1.5 Expectation values

1.6 Error propagation

2 Examples of probability functions

2.1 Binomial and multinomial distributions

2.2 Poisson distribution

2.3 Uniform distribution

2.4 Exponential disfribution

2.5 Gaussian distribution

2.6 Chi-square distribution

3 The Monte Carlo method

3.1 Uniformly distributed random numbers

3.2 The transformation method

3.3 The acceptance-rejection method

3.4 Applications of the Monte Carlo method

4 Statistical tests

4.1 Hypotheses, test statistics, significance level, power

4.2 An example with particle selection

4.3 Choice of the critical region using the Neyman-Pearson lemma

4.4 Constructing a test statistic

4.5 Goodness-of-fit tests

4.6 Pearson 's chi2 test

5 General concepts of parameter estimation

5.1 Samples, estimators, bias

5.2 Estimators for mean, variance, covariance

6 The method of maximum likelihood

6.1 ML estimators

6.2 Example of an ML estimator: an exponential distribution

6.3 Example of ML estimators

6.4 Variance of ML estimators: analytic method

6.5 Variance of ML estimators: Monte Carlo method

6.6 Variance of ML estimators: the RCF bound

6.7 Example of ML with two parameters

6.8 Testing goodness-of-fit with maximum likelihood

7 The method of least squares

7.1 Connection with maximum likelihood

7.2 Linear least-squares fit

7.3 Least squares fit of a polynomial

7.4 Least squares with binned data

7.5 Testing goodness-of-fit with chi2

9. Time series analysis

9.1 Time random processes

9.2 Relation to probability

9.3 Ensemble correlation functions

9.4 Time averages

9.5 Fourier trasform, discrete Fourier trasform Nyquist frequency and Sampling theorem

9.6 Power spectral density and its estimation

9.7 Response of linear filters, convolution theorem, aliasing and PSD windowing, correlation and autocorrelation

### Bridging Courses

There are no prerequisites.

### Learning Achievements (Dublin Descriptors)

Knowledge and understanding: the student will have to know the fundamental concepts of probability theory and be able to identify the appropriate statistical methodologies in the analysis of experimental data.
Applied knowledge and understanding: the student must be able to apply the methods studied to real problems by providing a correct statistical description of the experimental data and interpreting the results correctly.
Autonomy of judgment: the student must be able to independently evaluate the plausibility of the result of an analysis, both through the comparison between different possible approaches, and through analogical considerations and scientific common sense.
Communication skills: the student will have to acquire a correct scientific language and the ability to explain the statistical characteristics of the analyzed data.
Ability to learn: the student will be able to deepen specific concepts, not presented during the course, on scientific texts.

### Teaching Material

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

### Teaching, Attendance, Course Books and Assessment

Teaching

Lectures and classroom exercises.

Attendance

Attendance is strongly recommended.

Course books

Statistical Data Analysis - Glen Cowan - Oxford University Press

Detection of Signals in Noise - RN McDonough, AD Whalen - Academic Press

Assessment

Written test: problems of probability and statistics.

Oral test: questions on the entire program carried out.

Disabilità e DSA

Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.

A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.

### Additional Information for Non-Attending Students

Course books

Statistical Data Analysis - Glen Cowan - Oxford University Press

Detection of Signals in Noise - RN McDonough, AD Whalen - Academic Press

Assessment

Development of a project concerning the analysis of a set of data.

Written paper: structured tests, probability and statistics problem solving, open questions.

Oral questioning: questions about the entire program carried out.

EVALUATION CRITERIA AND PARAMETERS

For each item, four levels of assessment are given, corresponding to: insufficient (grade < 18); sufficient (17 < vote < 24); good (23 < vote < 28); excellent (27 < vote < 31)

Knowledge and understanding:

He does not know or roughly describes the topics covered

Describes with some inaccuracy the topics covered

Describes the topics in detail

Describes the topics in a precise and complete way

Applied knowledge and understanding

Does not know how to apply analysis procedures to sets of data

Apply the procedures in simple cases

Apply procedures in more complex cases

Apply the procedures and know how to relate them to different cases

Autonomy of judgments:

He is unable to assess the correctness of the procedure used and the plausibility of the results of an analysis.

Able to sufficiently evaluate the correctness of the procedure used and the plausibility of the results of an analysis

Able to evaluate the correctness of the procedure used and the plausibility of the results of an analysis.

Can evaluate the correctness of the procedure used and the plausibility of the results of an analysis and know how to contextualize the results.

Communication skills:

It is expressed in a non-specific common language

Demonstrates limited ability to express; use some specific terms

Demonstrates good ability to express and use some specific terms

Demonstrates full command of specific language

Disabilità e DSA

Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.

A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.

### Notes

The student must be able to apply the basic concepts of mathematical analysis.

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