Methods of data analysis

A.Y. 2018/2019
6
Max ECTS
42
Overall hours
SSD
FIS/01
Language
Italian
Learning objectives
This course introduces the students to the statistical analysis of experimental data and provides the basics for C++ applications in the ROOT environment with simple computer applications.
Expected learning outcomes
Undefined
Course syllabus and organization

Single session

Responsible
Lesson period
First semester
Course syllabus
Information from experimental data. Description of data. Descriptive and inferential statistics.Probability and its interpretation. Randon variables. Data description, probability density function, cumulative distribution function, covariance matrix, error propagation. Important distribution functions, law of large numbers, central limit theorem. Monte Carlo simulations. Point estimation of parameters. Estimation of mean, variance and covariance. Extended maximun likelihood and least squares methods. Interval estimation of parameters. Confidence intervals. Point and interval Bayesian estimators. Hypotheses testing, test statistics, Neyman-Pearson lemma, Goodness of fit tests. Pearson's χ^2 test and Kolmogorov-Smirnov test. Fisher discriminant, neural networks, boosted decision tree, and random forest. Theory-experiment comparison. Resolution function.
FIS/01 - EXPERIMENTAL PHYSICS - University credits: 6
Lessons: 42 hours
Professors: Neri Nicola, Palombo Fernando
Professor(s)