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  == Problem descriptions ==
 
   
  Add your problem description here for the discussion in the statistics reading club of 23 Feb
 
 
 
  ==== Wojtek ====
 
 
 
  [http://www.few.vu.nl/~wojtek/files/StatsSlides.pdf Hereby] a compilation of my slides that I was using during the first two meetings. Pay special attention to slides 1921  they contain tasks for some of you
 
  (RobertJan, Willem, Rob, Evert, Vincent) and give an idea of what to expect on Monday 21st (at 14:00).
 
 
 
  ==== Selmar/Gusz ====
 
  The dataset consists of 9 subsets of experimental data. In each subset the results are given of 100 runs of a specific EA on a specific problemfunction.
 
  There are three different EAs, and three different functions: Rastrigin, Sphere and a handcrafted stepped function.
 
 
 
  For each run the following metrics are saved:
 
  * Best fitness in the final population
 
  * The number of problem evaluations needed to find the solution
 
  * If the run terminated succesfully
 
 
 
  The question is: Which EA is the best?
 
 
 
  Download the dataset [http://www.cs.vu.nl/~sksmit/SRC.zip HERE] (zipped .MAT files)
 
 
 
  ==== Martijn ====
 
  [[Image:oanecstats.pngthumbexample results]]
 
 
 
  We investigate effects of reciprocity and transivity in network formation. The dataset consists of 3 X 4 X 5 subsets over three dimensions:
 
 
 
  * '''frequency_of_informal_opportunities''' \in [LOW, MEDIUM, HIGH]
 
  * '''[operational_transitivity, operational_reciprocity]''' \in [[no,no], [yes,no],[no,yes],[yes,yes]]
 
  * '''specialisation''' \in [admin, electro, nautical, technical, marines]
 
 
 
  We measured number of reciprocated ties.
 
 
 
  The question is: are the observed differences significant?
 
  Or: what dimension has largest impact on number of reciprocated ties?
 
 
 
  I have the dataset, but not readily available to include here.
 
 
 
  ==== Willem ====
 
  This is a problem I had with a previous paper.
 
 
 
  Stripped down, my problem comes down to the following:
 
  I have 3 algorithms: 2 benchmarks and 1 new algorithm. I want to show
 
  that my new algorithm outperforms the other algorithms. The output of
 
  the algorithms is a single number, the 'value'.
 
 
 
  The goal of the
 
  algorithm is to locate (static) targets, who are distributed over some
 
  terrain. I took 10 different target distributions, and tested each
 
  algorithm on these distributions, with 10 different initial random
 
  seeds.
 
 
 
  The problem here is that the random seed makes a lot of difference in
 
  how well the algorithms perform. This means that the mean performance
 
  of an algorithm over different random seeds doesn't give me much
 
  information. But, I can compare the outcomes of different algorithms
 
  using the same initial random seed.
 
 
 
  So, at each run, I computed the difference in value for new algorithm
 
  vs. the two benchmarks. To show that my algorithm outperforms the
 
  other algorithms, I now only have to show that these differences are
 
  significantly higher than 0. I did this using the wilcoxon signed rank
 
  test.
 
 
 
  Attached you will find 2 data sets and 2 plots. The first data file
 
  contains the difference between the new algorithm and the first
 
  benchmark, the second date file contains the difference between the
 
  new algorithm and the second benchmark. (the original outcomes of the
 
  algorithms are on a different computer than i'm on right now, so I
 
  cannot send you these.) Each row in the .dat files are the results for
 
  one target distribution.
 
 
 
  The two .pdf files are the plots for these differences. If you look at
 
  them, you intuitively see that the the differences are generally
 
  higher than 0. But, what is the best test to show this?
 
 
 
  [http://www.few.vu.nl/~willem/files/dumb_vs_smart.dat dumb_vs_smart.dat] (plain text)
 
  [http://www.few.vu.nl/~willem/files/dumb_vs_smart.pdf dumb_vs_smart.pdf] (pdf)
 
  [http://www.few.vu.nl/~willem/files/det_vs_smart.dat det_vs_smart.dat] (plain text)
 
  [http://www.few.vu.nl/~willem/files/det_vs_smart.pdf det_vs_smart.pdf] (pdf)
 