I've been reading out of A Student's Guide to Data and Error Analysis. This week I plan to do some work with the sample python code that is included in the book.

Did some practice work with the module used in the book. It's really clunky, so I decided to do some more work with the preloaded pylab graphing available in Ipython. It's much better.

I think for this upcoming week I'll try to get a simulation down and if I have time try to get a read on probability distributions and instrumental error.

Jim says :

We talked about mean and standard deviation and how to calculate them in numpy. I described the difference between \(\sigma\), the standard deviation, and \(s\), the best guess of the parent population's standard deviation.

For next time: do some numerical experiments with randomly chosen subsets of a parent population, in which you show that if sigma is the std dev of the subset, then sqrt(n)/sqrt(n-1) * sigma is a better estimate of the parent population sigma.

http://cs.marlboro.edu/ courses/ fall2014/jims_tutorials/ gschacht/ oct_23
last modified Thursday October 30 2014 2:41 pm EDT

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statstical_graphing_practice.ipynb Oct 30 2014 1:38 pm 30.8kB