Applied Statistics - Week 7

Monday the 11th - Friday the 15th of January 2021

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:

Monday:
The subject of the day will be fitting, moving towards advanced cases of fitting. As stated, this is a bit of an artform, and there is little literature on the subject - only (bitter?) experience!
Reading:

Possibly Barlow page 184, section 10.2.2.

Possibly Cowan page 65.

Possibly Bevington chapters 6-8 (best of the three!).
Lecture(s):

Advanced Fitting
Zoom: Link to lecture.
              Link to exercises.

Recording of Lecture video, Lecture audio, and Lecture chat.
Computer Exercise(s):

Advanced fitting: FitAndTestingDistributions_original.ipynb

Tuesday:
Statistical robustness in data reduction is important, when reducing the size of a (typically very) large data set to a more managable (i.e. "laptopable") size. The trick/pitfal is not to loose valuable information nor bias your sample in the proces. Lecture and exercise by Gabriel Brammer (also on his GitHub page).
Reading:

Data reduction is in the title of Bevington's book, yet modern data reduction goes a little beyond the scope of this (and most other) book(s). So no reading for this subject.
Lecture(s):

Detection and characterization of the most distant galaxies.

Illustration of Trail Factor concept.
Zoom: Link to lecture.
              Link to exercises.

Recording of Lecture video, Lecture audio, and Lecture chat.
Computer Exercise(s):

Image analysis: GalaxyImageAnalysis_original.ipynb
     Data files used in exericse: gnz11-hst_cutouts.npy, gnz11-hst_extra.npy, and scuba2.npy.

At the end of the exercise, we had a quick exercise discussion (video).

Friday:
The lecture will be a relatively thorough walk through the Problem Set. I'll go each problem, and discuss the solution. From this, we hope that our grading becomes clear.
In the exercises, we'll try a simple example of doing integration in many dimensions using simple simulation. First, it is the estimate of pi, followed by the rational numbers in front of (hyper) volumes of balls in many dimensions!
Reading:

A good introduction is in actually Wikipedia on Monte Carlo simulations.
Lecture(s):

Problem Set - Solutions, Comments, and Scores.
Zoom: Link to lecture.
              Link to exercises.

Recording of Lecture video, Lecture audio, and Lecture chat.
Computer Exercise(s):

Estimating pi and hypersphere size from simulation: PiEstimate_original.ipynb

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Last updated: 14th of January 2021.