Applied Statistics - Week 3

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 end of the week or when otherwise stated.

Monday:
We will consider Monte Carlo Techniques, which is a ubiquitious tool in data analysis. The central point is to be able to generate random numbers according to a given distribution, and subsequently use this.
Reading:

Glen Cowan: Chapter 3.

Particle Data Group (PDG) note on Monte Carlo.
Lecture(s):

Monte Carlo methods.
Computer Exercise(s):

TransHitMiss: TransHitMiss.py

Tuesday:
The main theme will be the Likelihood function, and the central role it plays in statistics. It is in principle the most powerful method for fitting, and estimation and ChiSquare can be derived from it.
Reading:

Barlow, chapter 5.3 to 5.7 (but not the proofs). In fact, by now you should have read Barlow, chapters 1-6!
Lecture(s):

Likelihood function
Computer Exercise(s):

LikelihoodFit: LikelihoodFit.py

Friday:
We will use the Friday for the HyperBall exercise and another example of producing random numbers according to a specific PDF. Furthermore, I'll go through the Table Measurement exercise in detail (and post a detailed solution!) and finally the lectures will for once be from 11-12 and a summary of what we have gone through so far.
Reading:

Possibly take a first look at Barlow, chapter 8.
Lecture(s):

Summary of curriculum so far.
Computer Exercise(s):

Pi Estimate: PiEstimate.py

Making Random Numbers: MakingRandomNumbers.py

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Last updated: 14th of September 2014.