Ateneo - UPO

The course will observe experimentally and verify some of the laws that are the subject of Physics I course or, using these laws, allow the student to experimentally measure some physical quantities. The student is introduced to the techniques of measurement, data analysis, evaluation of experimental uncertainty and to writing a laboratory report, which are the basis of the experimental work. Program: descriptive statistics: sample mean, median, mode, sample variance, standard deviation. Experimental uncertainties of type A and B. Uncertainty in indirect measurements. Plotting experimental data, covariance, correlation coefficient, linear regression. Elements of probability, histograms, continuous distributions, the gaussian distribution, confidence intervals, comparisons between measurements. Student’s correction.

John R Taylor,
Introduzione all'analisi degli errori -
Lo studio delle incertezze nelle misure fisiche,
Terza edizione, Zanichelli 2023 - ISBN: 978880839966;
G. Cannelli, Metodologie sperimentali in fisica, Terza edizione, EdiSES 2010 – ISBN: 9788879596794;
G. Ciullo, Introduzione al Laboratorio di Fisica - Misure e Teoria delle Incertezze, Springer-Verlag Italia, Milano 2014 – ISBN: 978-88-470-5655-8.

Introduction to the experimental method. Deepening of the understanding of physical laws studied in the “Physics I” course thanks to laboratory experiments, practice of programming, data representation and data analysis skills that are developed in the “Programming and data analysis laboratory” course.

No preliminary knowledge is required except that foreseen for the initial skills test; there is a connection with the course “Physics I” which is developed in parallel and with the course “Programming and data analysis laboratory”.

Lectures on experimental method, measurement uncertainties and statistical treatment of data; computer practice on statistical methods; laboratory practice with experiments concerning mechanics, thermodynamics and fluids.

Monitoring the learning process: this will be achieved by posing questions to students during lectures and laboratory practice, and also through quizzes proposed on the D.I.R. platform.

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti,
consulting the University webpage: https://www.uniupo.it/en/services/services-students-physical-or-learning-disabilities
Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

Evaluation of the written reports on the laboratory practice. Evaluation of the laboratory logbook. Oral examination on the physics principles of the laboratory practice and on the statistical analysis of data, discussion on the written reports.
To pass the test, the student must demonstrate knowledge and understanding of basic concepts and their applications to collect and analyze experimental data. Excellence is achieved if the laboratory reports are perfect, proving that the student has reached a level of knowledge and skill appropriate throughout all the course program, and proving to know clearly all the arguments required during the oral test.

The experimental method. Theory of measurement. Features of scientific instruments. Descriptive statistics. Uncertainty evaluation for direct measurements. Elements of probability theory. Limiting distribution and the Gaussian distribution. Probability distributions for discrete and continuous random variables. Properties of the Gaussian distribution. Computer practice on probability distributions. Moments, quantiles and percentiles of probability distributions. The Gaussian test for the comparison between two measurements. Chi-squared test. Introduction to le least squares method, application to the linear regression. Uncertainty evaluation for indirect measurements. Confidence intervals and Student’s correction. The binomial distribution. The Poisson distribution. Correlations, linear correlation coefficient and covariance. Laplace model for random measurement errors. The maximum likelihood principle and the derivation of the least squares method.
LABORATORY PRACTICE:
Introductive experiments: 1) measuring the period of a simple pendulum, 2) measuring the speed of sound.
Mechanics experiments:
1) Accelerated motion, 2) Sliding friction, 3) Stiffness constant of a spring, 4) Measuring g with Kater’s pendulum, 5) Density measurements.
Thermodynamics and fluid mechanics experiments:
1) Calorimetry measurements, 2) Law of cooling and phase transitions, 3) Gas laws, 4) Measuring surface tension, 5) Measuring viscosity and fluid dynamic drag.

- Knowledge and understanding: laboratory safety, use of laboratory instrumentation, improvement in the understanding of the basic principles of classical physics and on the statistical methods for the evaluation of the experimental uncertainty. Improvement of programming competences.
- Applying knowledge and understanding: ability to perform measurements in controlled conditions, to evaluate the experimental uncertainty, identify the issues that have an impact on the experimental precision and accuracy and to propose improvements in the experimental techniques. Apply the computing techniques to data analysis. Ability to work in a team.
- Communication skills: ability to organise team work (also as an introduction to the future work environment), ability to explain the experimental procedure and the results of an experiment in a lab report, using text, charts and tables.
- Learning skills: obtain a good knowledge of the experimental method in view of future studies and work. Ability to spot problems in the experimental procedures and identify problematic data.