Applied Statistics: From Data to Results

Updated: 7 months ago
Deadline: 01 Nov 2019

Applied Statistics: From Data to Results
PhD School at the Faculty of SCIENCE at University of Copenhagen
Formel 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
With this course, the student should 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.
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.
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.
Troels Christian Petersen ([email protected])
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.
•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.
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).
PhD students should enroll via this page.
MSc students: please go to to sign up for the MSc class.


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