Applied Statistics - Week 7

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:

This week is the last with exercises, and in addition to these, we will mainly focus on project 2.

Monday:
Fitting is an art, and the higher level of details wanted, and hence the more parameters involved, the greater of an art it is. Todays exercise is one in advanced fitting, introducing more and more parameters.

Reading:

There are no new things to read.
Lecture(s):

I will give a short introduction to todays exercise, but there are no slides.
Computer Exercise(s):

Advanced fitting exercise: HarmonicOscillatorFit.py,
data_HarmOsc1.txt,
data_HarmOsc2.txt,
data_HarmOsc3.txt,
Example solution: HarmonicOscillatorFit_solution.py,


Tuesday:
Apart from project 2, the subject of the day is time series, which is a branch of statistics dealing with series of data, typically ordered in time. I have chosen to omit this from curriculum, and the exercise is only for illustration, and mostly requires that you uncomment (some advanced) code.

Reading:
The following two links are for inspiration, and I suggest only reading the first parts.

Time Series (Wikipedia)

Auto Correlation (Wikipedia)
Lecture(s):

I will give a short introduction to todays exercise, but there are no slides.
Computer Exercise(s):

Time Series Analysis: timeSeries.py, functions timeSeries_functions.py, and data images.json.gz.
Solution example: timeSeries_sol.py and timeSeries_functions_sol.py

Friday:
Reading:

No reading - focus on project 2.
Lecture(s):

I will give a short introduction to todays exercise, but there are no slides.
Computer Exercise(s):

Random Digits Runs Test: RandomDigitsTest.py,
data_RandomDigits2011.txt,
data_RandomDigits2012.txt,
data_RandomDigits2013.txt,
data_RandomDigits2014.txt,
data_RandomDigits2015A.txt,
data_RandomDigits2015B.txt, and
data_RandomDigits2015C.txt
For a large scale test, try one million digits of pi: pi1000000.txt
Solution example: RandomDigitsTest_solution.py

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Last updated: 15th of January 2016.