Applied Statistics - Week 1
Monday the 21st - Friday the 25th of November 2016
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:
ShellCommands.tiff.pdf
Python and ROOT intro macro:
CalcPrimeNumbers.py
CalcPrimeNumbers.py
(very commented version)
Rolling Ball Timing Analysis:
RollingBallTimingAnalysis.py
data_RollingBall_example.txt
Rolling Ball Timing Analysis - as done in class:
RollingBallTimingAnalysis_AsInClass.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 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):
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
Error Propagation - numerical solution example::
ErrorPropagation_Solution.py
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):
Anscombe's Quartet:
AnscombesQuartet.py (just for illustration!)
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 2016.