Applied Statistics - Week 1

Monday the 19th - Friday the 23th of November 2018

ERDA shared link to full week material: h5UbTa7Luo

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.

Monday:
The first day of class will start 8.15 in Auditorium A, where I will give an introduction to the course, we will take photos (mug shots) of all of you, and you will measure the length of the front table.
Due to the large number of students in class, I have also booked Aud. B and D. Following the initial course introduction (8:15-9:00) and lecture on Central Limit Theorem (CLT), those with setup problems can go to Aud. B to sort these, while others can got to Aud. D for starting the exercise on CLT.
At 10:00 we will move to the BioCenter, where we'll work on the below exercise on Central Limit Theorem, which is the reason why the Gaussian (also called "Normal") distribution plays such a central role in statistics.

Reading:
  • Barlow, chapter 1, 2 (most of which you should know), and 4.1 + 4.2.
    Lecture(s):
  • Mean and Width.
  • Correlations.
  • Significant Digits.
  • Central Limit Theorem.
    Computer Exercise(s):
  • Central Limit Theorem: CentralLimit.ipynb
  • Anscombe's Quartet: AnscombesQuartet.ipynb (just for illustration!)

    Tuesday:
    The main theme will be the Error propagation, which most of you should know the basics of already. While error propagation is craftsmanship, there are never the less smart ways of doing it numerically.

    Reading:
  • Barlow, chapter 4.3.
    Lecture(s):
  • Error Propagation
    Computer Exercise(s):
  • Error Propagation: ErrorPropagation.py

    Friday:
    We will focus on the ChiSquare Method, which is basic method behind performing a fit to data. As it turns out, this method has the great advantage of providing a goodness-of-fit measure, which can be used to test, if the fit really resembles data.

    Reading:
  • Barlow, chapter 6.
    Lecture(s):
  • ChiSquare Test
    Computer Exercise(s):
  • ChiSquare Test: ChiSquareTest.py
  • ChiSquare Test - several examples: ChiSquareTest_SeveralExamples.py
    Notes:
  • Analytical Linear Fit note: StraightLineFit.pdf
  • Analytical Linear Fit implementation: AnalyticalLinearFit.py


    General notes, links, and comments:
  • Why Statistics: Monday morning, I tried to answer the question "Why Statistics?", and this presentation can be found here.
  • More Python intro: A good Python exercise is to consider the below program(s), which calculates and plots the distribution of prime numbers. As you surely know the math behind, you can see if you can also follow how it is programmed in Python below:
    Calculation: CalcPrimeNumbers.ipynb and plotting: CalcAndPlotPrimeNumbers.ipynb.

    Last updated: 20th of November 2018.