Physics, Physics Lab, Lab of Mathematical and Statistical Methodologies

Kinematics: linear and curved motions
Dynamics for the material point: principles and forces
Energy: work and energy conservation
Collisions: linear moment, elastic and anelastic collisions
Electrostatics: Coulomb force, electric field and potential, capacitor, Lorentz force.
Fluids: Static and dynamics of an ideal fluid, Laws of Pascal, Stevino, Archimede and Bernoulli.
Thermodynamics: Principles, ideal gases law, kinetic theory of an ideal gases, heat capacity, phase transitions, thermodynamics transformations, heat engines.

The course is partly lessons and partly laboratory. Lessons cover a short practical introduction to applied statistics and physics complements. the physics subjects treated are: electrical circuits (RC included); mechanical waves and electromagnetic waves spectrum, geometrical optics and elements of physical optics, elements of radioactivity.
The result of the laboratory is the measurement of the Faraday constant, with 2 different methods, of which precision, accuracy and compatibility are evaluated.

Descriptive statistics.
Population sampling. Types of data and variables. Subdivision of data into classes and creation of frequency tables. Histogram and histogram/bars. Centrality indexes (mean, mode, median, midrange), dispersion indexes (range, standard deviation, variance), percentiles, quartiles. Outliers. Boxplots.
Probability and random variables.
Sample space, events, probability of events. Probability of union and intersection of events. Complementary events. Independent events. Conditional probability. Bayes theorem. Factorial and binomial coefficients. Random variables. Expected value, variance, and standard deviation of discrete random variables. Discrete random variables: binomial and Poisson. Continuous random variables: uniform and normal. Standardization and calculations with the normal distribution. Percentiles. Normal approximation of binomial distribution.
Inferential statistics.
Fundamental concepts: population, sample, parameters, statistics, estimators. Sample mean behavior: large number law and central limit theorem. Confidence interval: general concepts. Confidence interval for a proportion. Confidence interval for the mean, both with known and unknown standard deviation. Student's distribution. Hypothesis test. General concepts: null and alternative hypothesis, first and second kind errors, significativity level, power function, p-value, statistics of the test, critical region. Test on the proportion. Test on the mean (both with known and unknown variance). Inference on two samples. Inference on two proportions. Inference on two means, both for independent and paired samples.
Linear regression and non-parametric procedures.
Covariance and correlation. Simple linear regression. Test on regression coefficients and model validation. Chi-square distribution. Test of independence and good adaptation.

Kinematics: linear and curved motions
Dynamics for the material point: principles and forces
Energy: work and energy conservation
Collisions: linear moment, elastic and anelastic collisions
Electrostatics: Coulomb force, electric field and potential, capacitor, Lorentz force.
Fluids: Static and dynamics of an ideal fluid, Laws of Pascal, Stevino, Archimede and Bernoulli.
Thermodinamics: Principles, ideal gases law, kinetic theory of an ideal gases, heat capacity, phase transitions, thermodynamics transformations, heat engines.

The course is partly lessons and partly laboratory. Lessons cover a short pratical introduction to applied statistics and physics complements. the physics subjects treated are: electrical circuits (RC included); mechanical waves and electomagnetical waves spectrum, geometrical optics and elements of physical optics, elements of radioactivity.
The final result of the laboratory is the measurement of the Faraday constant, with 2 different methods, of which precision, accuracy and compatibility are evaluated.

Descriptive statistics.
Population sampling. Types of data and variables. Subdivision of data into classes and creation of frequency tables. Histogram and histogram/bars.Centrality indexes (mean, mode, median, midrange), dispersion indexes (range, standard deviation, variance), percentiles, quartiles. Outliers. Boxplots.
Probability and random variables.
Sample space, events, probability of events. Probability of union and intersection of events. Complementary events. Independent events. Conditional probability. Bayes theorem. Factorial and binomial coefficients. Random variables. Expected value, variance and standard deviation of discrete random variables. Discrete random variables: binomial and Poisson. Continuous random variables: uniform and normal. Standardization and calculations with the normal distribution. Percentiles. Normal approximation of binomial distribution.
Inferential statistics.
Fundamental concepts: population, sample, parameters, statistics, estimators. Sample mean behavior: large number law and central limit theorem. Confidence interval: general concepts. Confidence interval for a proportion. Confidence interval for the mean, both with known and unknown standard deviation. Student's distribution. Hypothesis test. General concepts: null and alternative hypothesis, first and second kind errors, significativity level, power function, p-value, statistics of the test, critical region. Test on the proportion. Test on the mean (both with known and unknown variance). Inference on two samples. Inference on two proportions. Inference on two means, both for independent and paired samples.
Linear regression and non-parametric precedures.
Covariance and correlation. Simple linear regression. Test on regression coefficients and model validation. Chi-square distribution. Test of independence and good adaptation.