Applied Statistics - Week 5
Monday the 18th - Friday the 22nd of December 2023
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.
How to produce (great?) plots:
Plotting inspiration and code
Monday:
We will start the week by considering the theme
MultiVariate Analysis (MVA),
that is analysis of data with more than one (typically many) variables. To begin with, we will consider
the relatively simple linear case, which is described by (Fisher's) Linear Discriminant Analysis (LDA),
and then move on to more complex sets of data.
Reading:
Barlow, chapter 7
An additional possible source is Fisher’s Linear Discriminant: Intuitively Explained
Lecture(s):
Linear MultiVariate Analysis
Computer Exercise(s):
2par_discriminant.ipynb
fisher_discriminant.ipynb and
Fisher's Iris data.
Tuesday:
The day will focus on calibration, which is a subtle subject,
yet fairly straight forward, once you get the hang of the idea. The
associated exercise is inspired by typical data analysis work.
Reading:
No reading - logic and reason suffices.
Lecture(s):
Calibration
Computer Exercise(s):
Calibration: Calibration_original.ipynb (empty)
Calibration data file: data_calib.txt
Friday:
A central theme in probability and statistics is Bayes' Theorem, which concerns itself
with prior probabilities, i.e. incorporating existing knowledge in evaluating outcomes.
Many of you know this theorem already, but with this exercise we will try to bring a general
perspective on data analysis along with it.
In addition, we will have a look at Markov Chains and how they can be used in relation to
Bayes' Theorem. Mathias will be giving the lecture and have designed the exercises.
Reading:
Non - just listen in, ask questions, and wonder about these concepts.
NOTE: You should by now have read curriculum (roughly Barlow chapters 1-8).
Lecture(s):
Bayes' theorem and Markov Chains
Computer Exercise(s):
EhrenfestBallExperiment.ipynb.
DeterminingGenotypes.ipynb.
Last updated: 13th of December 2023.