First cycle
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Second cycle
degree courses
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School of Science
Course unit
EXPERIMENTS IN PHYSICS 1 (Iniziali cognome A-L)
SCO2045411, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course First cycle degree in
SC1158, Degree course structure A.Y. 2014/15, A.Y. 2019/20
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Number of ECTS credits allocated 13.0
Type of assessment Mark
Course unit English denomination EXPERIMENTS IN PHYSICS 1
Website of the academic structure
Department of reference Department of Physics and Astronomy
E-Learning website
Mandatory attendance
Language of instruction Italian
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

Teacher in charge CINZIA SADA FIS/03
Other lecturers DAVIDE DE SALVADOR FIS/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Other -- -- 4.0
Core courses FIS/01 Experimental Physics 9.0

Course unit organization
Period Annual
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Group didactic activities 0.0 36 0.0 No turn
Laboratory 5.5 84 53.5 No turn
Lecture 7.5 60 127.5 No turn

Start of activities 30/09/2019
End of activities 20/06/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
11 Sperimentazioni di Fisica 1 (Sdopp.) 01/10/2019 30/11/2020 DORO MICHELE (Presidente)
SADA CINZIA (Membro Effettivo)
GIAZ AGNESE (Supplente)
10 Sperimentazioni di Fisica 01/10/2019 30/11/2020 SADA CINZIA (Presidente)
DORO MICHELE (Membro Effettivo)
9 Sperimentazioni di Fisica 1 (iniziali cognome M-Z) 01/10/2018 30/11/2019 DORO MICHELE (Presidente)
SADA CINZIA (Membro Effettivo)
GIAZ AGNESE (Supplente)
8 Sperimentazioni di Fisica (iniziali cognome A-L) 01/10/2018 30/11/2019 SADA CINZIA (Presidente)
DORO MICHELE (Membro Effettivo)
GIAZ AGNESE (Supplente)

Prerequisites: Basic knowledge of:

- algebra;
- analysis (equations, derivatives, integrals, series);
- general physics (kinematic, dynamics, thermology).

The level knowledge in math and physics refers to high school courses.
Target skills and knowledge: The knowledge and skills to be gained at the end of the course are related to statistics and data analysis related to classical physics experiments (see the details of the knowledge by reading the section below).
In particular:

1. Understanding physical terminology in relation to the processing of experimental data and relative organization;
2. Methods for data analysis with random errors;
3. Direct and indirect measurement methods of the same physical size and of the best process of experimental data analysis;
4. Understanding the meaning of the approximations assumed and hypothesis tests;
5. understanding and estimating the causes of random error, role of systematic errors and their relative weight;
6. Quantifying the weight of the various error causes, focusing to appropriate sample size;
7. Skills in the use of computer software for data analysis;
8. Critical attitude in data processing (including the definition of the confidence interval of the experimental data);
9. Experimental skills;
10. Team-working
Examination methods: The exam consists of three parts:
1. Team Reports on laboratory experiments. Each report is delivered according to the calendar provided by the professors at the beginning of the laboratory. Delayed delivery will lead to penalties on the evaluation, no delivery prevents to access the interview (oral exam) with a positive result;
2. Written test (relating to Elements of Computer Science and Programming and Error and Statistical Analysis);
3. Oral evidence relating to Elements of Computer Science and Programming and theory of errors and statistics as well as on the critical discussion on the experiences carried out in the laboratory.
The final score is provided by the weighted average of the votes cast in the three parties.
Specifically, the written test will include exercises and demonstrations of Computer Elements, Programming (Part One) and Error and Statistical Theory (Part Two). It can be done in two ways: through overcoming the on-going tests or through the institutional exam sessions:
- 1st written test in "IT Elements and Programming Elements", Semester (December to January);
- 2nd written exam on the Elements of Theory of Errors and Statistics, topics presented in the lessons of the first semester (February-March);
- 3rd written test on Theory of Error and Statistics, topics presented in the lessons of the II semester (late May-early June).
The 2nd and 3rd round tests will be evaluated as a single test relative to the second part: the student's performance is considered sufficient, the average of the grades obtained during the two tests (2 ° -3 °) will be at least Equal to 18/30 (with a score of at least 15/30 on each).
Assessment criteria: Written and oral test evaluation criteria:

- Re-elaboration of knowledge and skills in relation to the course, including exercises;
- Communication skills (specific language skills, oral and / or written communication, how to present topics related to the course);

Evaluation Criteria of the Laboratory Activity

- Regularity in student attendance to the course;
- quality of the student contribute in the laboratory experiments;
- management of laboratory activities and participation to team work;
- re-elaboration of knowledge and skills developed in relation to laboratory contents;
- use of tools and materials provided during the course;
- discussion of reports;
- establishment and organization of relationships.
1) Information Theory. Positional numbering systems. Decimal, binary, octal and hexadecimal system. Basic change. Representation of the relative numbers: form and sign, complement to one, complement to two. Representation of rational numbers: Fixed and floating point representation. Standard IEEE-754.

2) Boolean Algebra, Sets Theory. Boolean algebra: definition and property. De Morgan's theorem. Boolean algebra with two elements {0,1}. The fundamental theorem of Boolean algebra. Corollaries.

3) Programming: Introduction to C ++. The main function. The #include directive. Using cin and cout operators. Declaring and initializing variables. Types of variables: char, int, long, float, double, bool. Arithmetic operators. Type Conversion. Numerical and relational expressions. Logical operators. The instruction if. Cycles: for, while, do-while. The conditional operator. The statement switch, break and continue. Array, Strings, Structure, and Pointers. Functions. Function prototypes. Inline Functions. References and pointers. Default topics. Overloading of functions. Template functions. Short introductions to classroom and object-oriented programming.

4) Laboratory Activity: Frequency is compulsory. In particular, the following topics will be addressed: Writing a C ++ first program. LINUX Tutorial. The structure of a program, introduction to flow charts. Writing a program for calculating the average, the area of ​​a triangle, the trajectory of a bullet. Programming exercises.

1) Direct and indirect measures. Measurement tools. Random and systematic errors. Accuracy, accuracy and sensitivity. Chance. Events and random variables, probability theorems and total probability. Bayes theorem. Examples and applications. Central trend estimates and dispersion estimates. Property. Histograms. Overlay a Guassian function on a histogram and data puncture.

2) Discrete random variables: generality. Populations and samples. Average value of linear combinations. Variance of linear combinations of statistically independent random variables. Bernoulli's Law and Theorem. Average value and true value. Relationship between sample variance and population variance. Continuous random variables: definition and properties, probability density, and distribution function. Properties of mathematical hope and variance. The uniform distribution, triangular and Gauss distribution. Combination calculus elements. Poisson distribution and its properties. The distribution of Bernoulli and its properties. The Chi-square distribution: definition and properties. Minimum method χ ^ 2. Χ ^ 2 applications and system constraints.Examples and applications.

3) Indirect measures
The propagation of errors and the limits of its validity. Maximum errors and maximum error propagation formula. Covariance and Linear Correlation and Related Properties.

4) Parameter estimation.
Likelihood function and method of maximum likelihood. Maximum likelihood estimation applications: weighted average and relative error, derivation of fitting parameters.

5) Laboratory activities with compulsory attendance on subjects of Mechanics, Thermology and Thermodynamics.
Planned learning activities and teaching methods:  
- frontal lessons, especially for computer science, introduction to the statistics and the presentation of physics experiments with the aim of abstract conceptualization of the various topics.

- the student will be given modeling tools, then leading to autonomy;

- group performance is best valued to promote collaborative relationships. In this context, the brainstorming methodology will also be used;

- collaborative learning: that is, learning in small groups, in which the students cooperate and feel responsible.
Additional notes about suggested reading: All the references will be provided by way of Moodle Laurea in Fisica
Textbooks (and optional supplementary readings)
  • Maurizio Loreti, Teoria degli errori e fondamenti di statistica (introduzione alla fisica sperimentale). --: Zanichelli, 2006. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Questioning
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

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