# Statistics and Data Analysis

### Objectives

In this course we will learn how to distill scientific knowledge from experimental data, a process that relies on statistical methods. We will learn the basics concepts of Probability and Statistics (in their Frequentist and Bayesian frameworks). In addition, we will study and practice several particular statistical methods and data analysis techniques usually used in the fields of High Energy Physics, Astrophysics and Cosmology. To that aim, we will learn and practice the use of modern statistics and analysis software tools.

### Skills

- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- Use mathematics to describe the physical world, select the appropriate equations, construct adequate models, interpret mathematical results and make critical comparisons with experimentation and observation.
- Use the adequate software, programming languages and computer packages to research problems related to high energy physics, astrophysics and cosmology.
- Work in a group and take on responsibility, interacting professionally and constructively with other people with complete respect for their rights.

### Learning outcomes

- Apply data analysis techniques to problems in the areas of particle physics, astrophysics and cosmology, as well as other close but different areas.
- Learn to use the Root statistical analysis tool.
- Use Monte Carlo techniques to model real problems of physics.
- Work in small groups to solve problems of data analysis.

### Content

- Basic concepts on probability, statistics and Monte Carlo techniques
- Python for Statistics and Data Analysis
- Parameter estimation, Hypothesis test and Unfolding
- Bayesian Statistics

### Prerequisites

For the Python Bootcamp (part 2), it is highly needed to bring a personal laptop with a running installation of Python 3.6. For that, install Python 3.6 with the Anaconda installer. In this way, your Python distribution will contain all the associated packages needed for this course.

Follow these steps:

- Download Anaconda installer for Python 3.6 here https://www.anaconda.com/download/
- Follow the installation instructions - both GUI or terminal versions work fine. If prompted, select the option to add the new anaconda directory to your path.
- The use of Linux or Mac is highly recommended.

### Details

Semester | 1 |

Itinerary | HEP, ASTRO |

Type | Mandatory |

ECTS | 9 |

Hours | 56 |

### Teachers

- Javier Rico
- Ramon Miquel
- Abelardo Moralejo
- Jorge Carretero
- Santiago Serrano
- Pau Tallada
- Francesc Torradeflot

### Bibliography

- G. Cowan; “Statistical Data Analysis”, 1998, Oxford University Press
- K. A. Olive et al. (Particle Data Group); “Review of Particle Physics”, Chin. Phys. C38 (2014) 090001 A.
- Bevan; “Statistical Data Analysis for the Physical Science”, 2013, Cambridge
- F. James; “Statistical Methods in Experimental Physics”, 2nd Edition, 2006, World Scientific
- L. Lyons, “Statistics for Particle and Nuclear Physicists”, 1986, Cambridge University Press
- B. P. Roe, “Probability and Statistics in Experimental Physics”, 1992, Springer
- A. G. Frodesen, et al., “Probability and statistics in particle physics”, 1979, Columbia University Press D.
- Sivia and J. Skilling, “Data Analysis, A Bayesian Tutorial”, 2nd ed., 2006, Oxford University Press A.
- Gelman, “Bayesian Data Analysis”, 1995, CRC Press
- R. J. Barlow, “Statistics”, 1989, J. Wiley
- W.T. Press et al., “Numerical Recipes: The Art of Scientific Computing”, Cambridge University Press.
- E.T. Jaynes, “Probability Theory: The Logic of Science”, Cambridge University Press.
- A. Stuart et al., “Kendall’s Advanced Theory of Statistics”, Vol 2A. Wiley.
- F. James, “Monte Carlo Theory and Practice”, Rep. Prog. Phys. 43 (1980) 73.