Applied Statistics - Week 1

Monday the 16th - Friday the 20th of November 2015

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:
  • Introduction to Python: pythonIntro.py
  • Introduction to ROOT: RootIntro.py
  • Shell commands: shell_commands.pdf
  • Python and ROOT intro macro: CalcPrimeNumbers.py CalcPrimeNumbers.py (very commented version)

    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 table by the blackboard. At 10:00 we will move to Auditorium M, where there will be the below lecture and exercise on Central Limit Theorem, and 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):
  • Significant Digits.
  • Mean and Width.
  • Correlations.
  • Central Limit Theorem.
    Computer Exercise(s):
  • Anscombe's Quartet: AnscombesQuartet.py (just for illustration!)
  • Central Limit Theorem: CentralLimit.py

    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
  • Error Propagation - analytical solution example: Note_ErrorPropagationSolution.pdf
  • Pendulum Error Estimate: PendulumErrorEstimate.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: AnalyticalLinearFit.py
  • Analytical Linear Fit note: StraightLineFit.pdf
    Last updated: 20th of November 2015.