Applied Statistics - Week 5

Monday the 14th - Friday the 18th of December 2020

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.

NOTE: Sunday the 13th (at 22:00) is the deadline for the project. At the end of the week, we will by then essentially be done with most of the core curriculum, and I will not introduce anything fundamentally new on Friday, which will be dedicated to the discussion of the project and catching up on exercises (or Problem Set). Please remember to submit your pendulum timing residuals.

And if you felt that making a precision measurement was hard, then please read this short passage from "The Measure of All Things" about the making of the meter, where one of the two main persons behind the measuring of the French meridian, Delembre, is struggling to get precise results.

General notes, links, and comments:
  • How to produce (great?) plots: Plotting inspiration and code


    Monday:
    We will use two days for the last "major" theme in this course, which is 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 Discriminant, and then move on to more complex sets of data, for which more advanced non-linear methods, such as Neural Networks (NN) and Boosted Decision Trees (BDT) are more useful.

    Reading:
  • NOTE: You should by now have read curriculum (roughly Barlow chapters 1-8).
    Lecture(s):
  • Bayes Theorem
  • MultiVariate Analysis - Part I
    Zoom: Link to lecture.
                  Link to exercises.
  • Recording of Lecture video, Lecture audio, and Lecture chat.
    Computer Exercise(s):
  • 2par_discriminant.ipynb
  • fisher_discriminant.ipynb and 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
    Zoom: Link to lecture.
                  Link to exercises.
  • Recording of Lecture video, Lecture audio, and Lecture chat.
  • Recording of Exercise recap video and Exercise recap audio.
    Computer Exercise(s):
  • Calibration: Calibration_original.ipynb
  • Calibration data file: data_calib.txt

    Friday:
    On Friday the 18th of December (last day of course in 2020), I will not introduce anything new, but simply give a short summary/overview of the course so far and a review of the project experiments. In the exercise session, we will simply catch up and ask all the questions you might like to resolve before Christmas.

    Reading:
  • You should by now have read curriculum (roughly Barlow chapters 1-8).
    Lecture(s):
  • Project experiments review
    Zoom: Link to lecture.
                  Link to exercises.
  • Recording of Lecture video, Lecture audio, and Lecture chat.
    Computer Exercise(s):
  • Catch up on previous exercises (perhaps in particular those related to the Problem Set!).
    Last updated: 18th of December 2020.