PHYSICS LABORATORY 1 (UNIT 2)
LEARNING OUTCOMES OF THE COURSE UNIT
Acquirement of basic measurement techniques and methodologies for experiments in Mechanics and Calorimetry, including software for data acquisition and analysis. Theory of errors and basics of probability theory. Acquirement of measurement techniques and methodologies concerning the proposed experiments.
Some basic concepts of math: algebra, trigonometry, analytic geometry, differential and integral calculus.
Some basic concepts in physics: kinematics and dynamics of material point, calorimetry.
COURSE CONTENTS SUMMARY
Basics of theory of probability.
Distribution functions for discrete and continuous random variables.
Estimators and their properties.
Statistical hypothesis testing
- motion of rigid bodies
- motion of pendulum
- fluid mechanics
- waves in continuous media
- calorimetry and phase transitions
1. J.R. Taylor, Introduzione all'Analisi degli Errori, Ed. Zanichelli, Bologna, 2° ed., 2000.
2. M. Loreti, Teoria degli errori e fondamenti di statistica, http://wwwcdf.pd.infn.it/labo/INDEX.html (2005).
3. Materiale fornito dal docente.
ASSESSMENT METHODS AND CRITERIA
In Itinere evaluations. Oral examinations.
The laboratory work is accounted for by written reports, one for each weekly laboratory experiment. At the end of the course an oral examination and, in case of not positive evaluation during the course, a laboratory experience might be required.
Lectures and laboratory activity. Lectures, laboratory exercises, lectures on computer (software facilities, scientific computing, acquisition and treatment of data, experiment simulations).
The course is split up into two periods: 6 CFU in the first semester and 6 CFU in the second semester. There is a single final exam at the end of the second semester.
1. Basics of theory of probability: statistics and probability. Short account on the axiomatic theory of probability: axioms of Kolmogorov.
2. Fundamental theorems of the theory of probability: addition and multiplication of events; complement of an event; dependent and independent events; conditional probability. Addition and multiplication rules for independent and dependent events; total probability theorem; Bayes’ formula. Repeated trials: Bernoulli trials, binomial law. Short account on the deduction of the theorems in the frame of axiomatic theory of probability.
3. Probability distributions: distribution laws, cumulative distribution functions and probability density; estimators and their properties: mean, median, mode; moments of a distribution, asymmetry and kurtosis. Chebishev inequality.
4. Discrete probability distributions: discrete uniform distribution; binomial distribution: moments, recurrence relations; Poisson distribution: moments. Radioactive decays.
5. Continue probability distributions: continue uniform distribution; Gauss distribution; standardized gaussian distribution; moments; gaussian approximation of binomial and Poisson distributions. Central limit theorem. Chi-squared distribution. Cauchy distribution.
6. Gaussian distribution: maximum likelihood criterion: mean as the best estimate, standard deviation, standard deviation of the mean, weighted average. Demonstrations of relations for error propagation: basic operations, sum of squared errors, general formula.
7. Applications to data treatment: least squares fitting and regression, linear fitting, weighted least squares fitting; non-linear fitting. Multiple stochastic variables, marginal density, stochastic independence, covariance; covariance and error propagation. Correlation: linear correlation coefficient.
8. Applications to data treatment: consistency tests: significance level, chi-squared test; consistency of a distribution.
The laboratory experiments will cover the following subjects:
• motion of rigid bodies
• motion of pendulum
• torsional oscillations
• damped and forced oscillatory motion
• fluid mechanics
• waves in continuum media
• calorimetry and phase transitions