Applied Statistics - Week 1

Monday the 20th - Friday the 24th of November 2017

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 and plotting: PythonIntro.py and IntroToPlottingAndFitting.py.

Shell commands: ShellCommands.tiff.pdf

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.py
Calculation and plotting: CalcAndPlotPrimeNumbers.py.

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. At 10:00 we will move to A110 and A111, where there will be the below 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):

Central Limit Theorem: CentralLimit.py

Anscombe's Quartet: AnscombesQuartet.py (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

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Last updated: 17th of November 2017.