Applied Statistics - Week 6
Monday the 3rd - Friday the 7th of January 2022
The following is a description of what we will go through during this
week of the course. The chapter references and computer exercises are
considered read, understood, and solved by the beginning of the
following class, where I'll shortly go through the exercise
solution.
General notes, links, and comments:
Lady tasting tea (Wikipedia).
Short note on Lady tasting tea.
>
Monday:
We will spend both Monday and Tuesday on a larger exercise, which
illustrates the idea of separating data into catagories, and how to
measure and optimise the performance of this in real data
with all of its quirks and twists.
The data is from ATLAS
testbeam data at CERN and deals with separating particles in a beam into
electrons and pions, but could in principle be from any other area of research
and/or business.
The subject is associated with Bayes' Theorem, as there are not an equal
amount of each type of particle in the beam. Many of you know this theorem already,
but with this exercise I'll try to bring a general perspective on data analysis
along with it.
In addition, we'll have a look at Markov Chains and how they can be used in
relation to Bayes' Theorem.
Reading:
Barlow, chapter 7
Lecture(s):
Test beam data introduction
I will give an introduction to todays exercise on
ATLAS testbeam data.
Zoom:
Link to lecture.
Recording of Lecture video.
Link to exercises.
Computer Exercise(s):
Analysis of ATLAS testbeam data:
ATLAStestbeam.ipynb along with
main data (2 GeV) and
alternative data (9 GeV).
Tuesday:
Reading:
No reading - focus on ATLAS test beam data analysis.
Lecture(s):
MultiVariate Analysis3 - Part III
Zoom:
Link to lecture.
Recording of Lecture video.
Link to exercises.
Computer Exercise(s):
We will continue with the ATLAS testbeam exercise, getting some key figures.
Analysis of ATLAS testbeam data:
ATLAStestbeam.ipynb along with
main data (2 GeV) and
alternative data (9 GeV).
Friday:
Mathias is in charge of Friday's subject, which is on Markov Chains.
Reading:
Non - just listen in, ask questions, and wonder about this concept.
Lecture(s):
Bayes and Markov Chains
Zoom:
Link to lecture.
Link to exercises.
Computer Exercise(s):
EhrenfestBallExperiment.ipynb
Determining_Genotypes.ipynb (additional/optional exercise)
Last updated: 3rd of January 2022.