Faculty of Science

Applied Statistics: From Data to Results
Provider: Faculty of Science

Activity no.: 5888-18-11-31
	Enrollment deadline: 18/11/2018

Place	Niels Bohr Institute
Date and time	19.11.2018, at: 08:00 - 27.01.2019, at: 16:00
Regular seats	60
ECTS credits	7.50

Contact person	Julie Meier E-mail address: juliemh@nbi.ku.dk

Enrolment Handling/Course Organiser	Troels Christian Petersen E-mail address: petersen@nbi.ku.dk
Written language	English
Teaching language	English
Study board	Study Board of Physics, Chemistry and Nanoscience
Semester/Block	Block 2
Scheme group	B
Exam form	Continuous assessment
Exam form	Written assignment, 28 timer
Exam details	The final grade is normally given based on the continuous evaluation as well as on the take-home exam with the following weight; 25% from projects, 15% from problem sets, and 60% from 28 hours take-home exam. It is possible to some extent to arrange a different weight in individual cases in agreement between the student and course responsible, if this can be justified.
Grading scale	Passed / Not passed
Exam re-examination	The exam form is identical to the regular exam; projects and problem sets that were approved during the course can be re-used. The remaining projects and problem sets should be approved 2 weeks before the re-exam.

Enrolment guidelines The course will give the student an introduction to and a basic knowledge on statistics. The focus will be on application and thus proofs are omitted, while examples and use of computers take their place. The course will cover the following subjects: •Introduction to statistics. •Distributions - Probability Density Functions. •Error propagation. •Correlations. •Monte Carlo - using simulation. •Statistical tests. •Parameter estimation - philosophy and methods of fitting data. •Chi-Square and Maximum Likelihood fits. •Simulation and planning of an experiment. •The power and limit of statistics. The frontier. Formal requirements Programming is an essential tool and is therefore necessary for the course (we will use Python with interface to CERN’s ROOT software, both free and working on all platforms). The student should be familiar with different types of variables, loops, if-sentences, functions, and the general line of thinking in programming. Elementary mathematics (calculus, linear algebra, and combinatorics) is also needed. Learning outcome Skills The student should in the course obtain the following skills: • Determining mean, width, uncertainty on mean and correlations. • Understading how to use probability distribution functions. • Be able to calculate, propagate and interprete uncertainties. • Be capable of fitting data sets and obtain parameter values. • Know the use of simulation in planing experiments and data analysis. Knowledge The student will obtain knowledge about statistical concepts and procedures, more specifically: • Binomial, Poisson and Gaussian distributions and origins. • Error propagation formula and how to apply it. • ChiSquare as a measure of Goodness-of-fit. • Calculation and interpretation of ChiSquare probability. Competences This course will provide the students with an understanding of statistical methods and knowledge of data analysis, which enables them to analyse data in ALL fields of science. The students should be capable of handling uncertainties, fitting data, applying hypothesis tests and extracting conclusions from data, and thus produce statistically sound scientific work. Literature See Absalon for final course material. The following is an example of expected course literature. Primary literature: Statistics - A Guide to the Use of Statistical Methods in the Physical Sciences, Roger Barlow. Additional literatur: Statistical Data Analysis, Glen Cowan. Data Reduction and Error Analysis, Philip R. Bevington. Statistical Methods in Experimental Physics. Lecturers Troels Christian Petersen (petersen@nbi.ku.dk) Remarks It is expected that the student brings a laptop. There will be an introduction the week before the course begins. You will be informed about time and place later (on the course webpage).

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