Applied Statistics - Week 5

Monday the 19th - Tuesday the 20th of December 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.

How to produce (great?) plots: Plotting inspiration and code


Monday:
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 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. Mathias will be giving both 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
Zoom:

Link to lecture.
Recording of Lecture video.
Computer Exercise(s):

EhrenfestBallExperiment.ipynb.

DeterminingGenotypes.ipynb.

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
Zoom:

Link to lecture. Computer Exercise(s):

Calibration: Calibration_original.ipynb

Calibration data file: data_calib.txt

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Last updated: 15th of December 2022.