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PEOPLE!!! Are you hackers or not!?!? Download the Excel spreadsheet and look at the raw numbers. The numbers in columns A,D and E have 2 significant digits after the decimal point, whereas the numbers in columns B,C and F have 13 significant digits after decimal point! No calculations necessary!
Standard dev seamed to be enough. I sorted and graphed the data to to be sure.
In a more general sense, I would tend to use a Self Organising Map (http://en.wikipedia.org/wiki/Self-organizing_map) to identify the two groups of data, and then discrimante between them. The dimensions of the SOM would be different statistical analyses of the data sets - what is the mean, the absolute spread, the standard deviation, the median, etc.
The nice thing about using a SOM is that it will show you whether or not you have succeeded in finding a measure that can successfully discriminate between different data sets - whilst at the same time actually doing the discrimination for you.
I would use the Kolmogorov-Smirnov test: http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test It's in scipy.stats.ks_2samp Results: Sets| D |p-value ----------------- A,C |0.275|0.080| B,C |0.175|0.531| ----------------- A,D |0.125|0.893| B,D |0.275|0.080| ----------------- A,E |0.100|0.983| B,E |0.300|0.043| ----------------- A,F |0.300|0.043| B,F |0.100|0.983| ----------------- As far as the test goes, if D…
there is a 98.3% chance that f is b-like there is a 98.3% chance that e is a-like there is a 89.3% chance that d is a-like there is a 53.1% chance that c is b-like
If these are multiplied together, it appears that there is only a 45.8% chance that they are all classified correctly?
PEOPLE!!! Are you hackers or not!?!? Download the Excel spreadsheet and look at the raw numbers. The numbers in columns A,D and E have 2 significant digits after the decimal point, whereas the numbers in columns B,C and F have 13 significant digits after decimal point! No calculations necessary!
I would use my right hand to slap Alison and Charlie and get them to do their work again... this time forcing them to label their samples. I would then take them into my office and hold an inquest on how two completely different data-sets got mixed up in the first place. At which point I would probably find out Alison and Charlie have been knocking boots on company time.. Alison and Charlie would then be fired as thi…
I would use the Kolmogorov-Smirnov test: http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test It's in scipy.stats.ks_2samp Results: Sets| D |p-value ----------------- A,C |0.275|0.080| B,C |0.175|0.531| ----------------- A,D |0.125|0.893| B,D |0.275|0.080| ----------------- A,E |0.100|0.983| B,E |0.300|0.043| ----------------- A,F |0.300|0.043| B,F |0.100|0.983| ----------------- As far as the test goes, if D…
Actually, just looking at the variance of the columns tells the same story as you've discovered above:
> var(data) A B C D E F 117.19610 20.49239 33.13114 90.62195 115.39044 27.34298
A, D, E are in the same group, B, C, F are in the same group.
Several people have said they would, or have, ploted histograms - but how? * By hand? * With Excel? * With R? * With processing? I'm a little sad to see that this item has been flagged heavily, but I guess there are people who think this is sufficiently off-topic that it should be in the same category as spam.
use R #copy data into a txt file without the first two lines from the xls par(mfrow = c(3,2)) dat for(let in c("A","B","C","D","E","F")) plot(density(dat[,let]), main = let) easy...(why doesnt HN recognise newlines?)
It treats single newlines as the same paragraph and double newlines as a new one. Alternatively, if you want to post code, you can indent it by four spaces. Like this:
par(mfrow = c(3,2))
dat