
Course unit
EXPERIMENTS IN PHYSICS 1 (Iniziali cognome AL)
SCO2045411, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary 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 
Hours of Individual study 
Shifts 
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 
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)
BRAGGIO
CATERINA
(Supplente)
GIAZ
AGNESE
(Supplente)
MENGONI
DANIELE
(Supplente)

10 Sperimentazioni di Fisica 
01/10/2019 
30/11/2020 
SADA
CINZIA
(Presidente)
DORO
MICHELE
(Membro Effettivo)
BRAGGIO
CATERINA
(Supplente)
DE SALVADOR
DAVIDE
(Supplente)
MAZZOCCO
MARCO
(Supplente)

9 Sperimentazioni di Fisica 1 (iniziali cognome MZ) 
01/10/2018 
30/11/2019 
DORO
MICHELE
(Presidente)
SADA
CINZIA
(Membro Effettivo)
BRAGGIO
CATERINA
(Supplente)
GIAZ
AGNESE
(Supplente)
MAZZOCCO
MARCO
(Supplente)
MENGONI
DANIELE
(Supplente)

8 Sperimentazioni di Fisica (iniziali cognome AL) 
01/10/2018 
30/11/2019 
SADA
CINZIA
(Presidente)
DORO
MICHELE
(Membro Effettivo)
BRAGGIO
CATERINA
(Supplente)
GIAZ
AGNESE
(Supplente)
MAZZOCCO
MARCO
(Supplente)
MENGONI
DANIELE
(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. Teamworking 
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 ongoing 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 (FebruaryMarch);
 3rd written test on Theory of Error and Statistics, topics presented in the lessons of the II semester (late Mayearly 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:
 Reelaboration 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;
 reelaboration 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. 
Course unit contents:

COMPUTING AND PROGRAMMING
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 IEEE754.
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, dowhile. 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 objectoriented 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.
INTRODUCTION TO THEORY OF ERRORS
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 Chisquare 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
https://www.elearning.unipd.it/fisica Laurea in Fisica 
Textbooks (and optional supplementary readings) 

Maurizio Loreti, Teoria degli errori e fondamenti di statistica (introduzione alla fisica sperimentale). : Zanichelli, 2006. http://wwwcdf.pd.infn.it/labo/

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, ...)
Sustainable Development Goals (SDGs)

