Tuesday, April 13, 2010
Star Plot
A star plot allows for the comparison of multiple variables of different types of data. For example, here the model is comparing 1-9 different types of data of 16 different cars. Note the similarities between the Buick Electra and the Cadillac DeVille.
(http://images.google.com/imgres?imgurl=http://www.itl.nist.gov/div898/handbook/eda/section3/gif/starplot.gif&imgrefurl=http://www.itl.nist.gov/div898/handbook/eda/section3/starplot.htm&usg=__brRSSJMv9BmxEcBW7Lip5DAHakA=&h=280&w=380&sz=4&hl=en&start=2&um=1&itbs=1&tbnid=Zy5ubhyj-artSM:&tbnh=91&tbnw=123&prev=/images%3Fq%3D%2522star%2Bplot%2522%26um%3D1%26hl%3Den%26client%3Dfirefox-a%26sa%3DG%26rls%3Dorg.mozilla:en-US:official%26tbs%3Disch:1)
Correlation Matrix
This matrix correlates the different attributes of wine.
http://images.google.com/imgres?imgurl=http://rattle.togaware.com/rattle-correlation.png&imgrefurl=http://tolstoy.newcastle.edu.au/R/e2/help/06/09/1249.html&usg=__veSd2Vj5q9yhWfMfxJC2OX_W1z0=&h=492&w=480&sz=121&hl=en&start=16&um=1&itbs=1&tbnid=RmrwbvVOLZ9v1M:&tbnh=130&tbnw=127&prev=/images%3Fq%3DCorrelation%2BMatrix%26um%3D1%26hl%3Den%26safe%3Doff%26client%3Dfirefox-a%26sa%3DN%26rls%3Dorg.mozilla:en-US:official%26tbs%3Disch:1
http://images.google.com/imgres?imgurl=http://rattle.togaware.com/rattle-correlation.png&imgrefurl=http://tolstoy.newcastle.edu.au/R/e2/help/06/09/1249.html&usg=__veSd2Vj5q9yhWfMfxJC2OX_W1z0=&h=492&w=480&sz=121&hl=en&start=16&um=1&itbs=1&tbnid=RmrwbvVOLZ9v1M:&tbnh=130&tbnw=127&prev=/images%3Fq%3DCorrelation%2BMatrix%26um%3D1%26hl%3Den%26safe%3Doff%26client%3Dfirefox-a%26sa%3DN%26rls%3Dorg.mozilla:en-US:official%26tbs%3Disch:1
Similarity Matrix
This matrix compares similarities between different types of organisms and inanimate objects. The darker areas suggest a similarity.
(http://images.google.com/imgres?imgurl=http://www.lems.brown.edu/vision/people/leymarie/Refs/CompVision/Ridges/Figs/similar.gif&imgrefurl=http://www.lems.brown.edu/vision/people/leymarie/Refs/CompVision/Ridges/Shinagawa.html&usg=__7EGr-MwoenD8D0c4EFU2TN3x-lc=&h=594&w=731&sz=78&hl=en&start=26&um=1&itbs=1&tbnid=5SEo0mmVgBwleM:&tbnh=115&tbnw=141&prev=/images%3Fq%3Dsimilarity%2Bmatrix%26start%3D18%26um%3D1%26hl%3Den%26client%3Dfirefox-a%26sa%3DN%26rls%3Dorg.mozilla:en-US:official%26ndsp%3D18%26tbs%3Disch:1)
Stem and Leaf Plot
These types of plots are a valuable way to show small amounts of information.
(http://images.google.com/imgres?imgurl=http://www.qub.ac.uk/lskills/TLTP3/WN/stem%2520and%2520leaf.gif&imgrefurl=http://www.qub.ac.uk/lskills/TLTP3/WN/NumeracyDiagrms.html&usg=__YXKvgOpuJ8mkwVuVSp_kIzLdHX0=&h=334&w=284&sz=5&hl=en&start=46&um=1&itbs=1&tbnid=mmt_11itfgvCGM:&tbnh=119&tbnw=101&prev=/images%3Fq%3Dstem%2Band%2Bleaf%2Bplot%26start%3D36%26um%3D1%26hl%3Den%26client%3Dfirefox-a%26sa%3DN%26rls%3Dorg.mozilla:en-US:official%26ndsp%3D18%26tbs%3Disch:1)
Box Plot
"Box Plot representation of distances covered by First Division Brazilian soccer players (n = 55) according to playing positions after 90 minutes of play, including only those who played the whole game. The players were classified in five positional groups: central defenders (CD, n = 15), external defenders (ED, n = 12), central midfield players (CM, n = 11), external midfield players (EM, n = 9) and forwards (F, n = 8)."
(http://www.jssm.org/vol6/n2/11/F4.htm)
This type of data representation is good in understanding groups of numerical data. It looks like the Central Defender came in first.
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