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# Data Analysis

## Module PH2221

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

### Module version of WS 2019/20 (current)

There are historic module descriptions of this module. A module description is valid until replaced by a newer one.

Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.

available module versions
WS 2019/20WS 2017/8SS 2015

### Basic Information

PH2221 is a semester module in English or German language at Master’s level which is offered in winter semester.

This Module is included in the following catalogues within the study programs in physics.

• Specific catalogue of special courses for nuclear, particle, and astrophysics
• Specific catalogue of special courses for Applied and Engineering Physics
• Complementary catalogue of special courses for condensed matter physics
• Complementary catalogue of special courses for Biophysics

If not stated otherwise for export to a non-physics program the student workload is given in the following table.

150 h 60 h 5 CP

Responsible coordinator of the module PH2221 is Allen C. Caldwell.

### Content, Learning Outcome and Preconditions

#### Content

You will learn how to formulate your statistical data analysis, from identifying the correct underlying statistical model, to defining your data probability functions (likelihoods) and prior probabilities.  You will learn when Gaussian approximations are valid, the derivation and use of the Central Limit theorem, how to define and use test statistics, goodness-of-fit tests, model selection, etc.

#### Learning Outcome

After successful completion of this module, the student is able to

• derive and know applicability of basic statistical distributions (Binomial, Poisson, Gauss, …).
• formulate a statistical analysis in the Bayesian, Frequentist of Likelihood approach.
• derive and know applicability of the Central Limit theorem.
• define and use test statistic.
• perform goodness-of-fit tests and model selection.

#### Preconditions

No preconditions in addition to the requirements for the Master’s program in Physics.

### Courses, Learning and Teaching Methods and Literature

#### Courses and Schedule

VO 2 Data Analysis Caldwell, A. Mon, 16:00–18:00, PH II 127
eLearning
UE 2 Exercise to Data Analysis Krätzschmar, T.
Responsible/Coordination: Caldwell, A.
dates in groups eLearning

#### Learning and Teaching Methods

The lectures will present the learning content (in English).  Examples will be drawn from a range of physics areas.  A number of exercises will be assigned that the students will be expected to solve over the course of the semester and submit in a written report.

A recitation session will precede the lectures, where students will present their solutions to the exercises and where further examples will be presented.

#### Media

A script for the course will be provided.  You will additionally need a computer.

#### Literature

• G. d'Agostini, "Bayesian reasoning in data analysis - A critical introduction", World Scientific Publishing 2003 (soft cover 2013).
• G. Cowan, "Statistical Data Analysis" (Oxford Science Publications) 1st Edition.
• W. T. Eadie, D. Dryard, F. E. James, M. Roos e B. Sadoulet, "Statistical methods in experimental physics", North-Holland Publishing Company, Amsterdam, London, 1971.
• E. T. Jaynes, `Probability Theory: The Logic of Science', Cambridge University Press, 2003.

### Module Exam

#### Description of exams and course work

The achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using presentations independently prepared by the students. The exam of 25 minutes consists of the presentation and a subsequent discussion.

#### Exam Repetition

The exam may be repeated at the end of the semester.

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