Principle component analysis

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Question:
<br><br> principle component analysis
<br><br>Posting the experiment went as follows: 6 samples were given 18 attributes following a sensory evaluation test.
<br><br>these have been put into s selection of charts - the first has the Plot of loadiing and has the 18 attributes dotted across the chart, X axis is PC1 and Y axis is PC2. a second chart has the same but has the 6 samples dotted across the chart. after that i have a new selection of chart but they have X axis is PC2 and Y is PC3, again with the figures dotted on the chart.
<br><br>I have been asked to interpret these figures but i dont know how. PLease can you help
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<br><br>I will try to explain the fundamental ideas here. Your question was also vaguely stated. I am therfore trying my level best to understand your queries and am trying to help you.
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<br><br>Principle Component Analysis
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<br><br>The multivariate statistical method of Principle Component Analysis is a very useful tool for reducing the number of variables in a data set and for obtaining useful two-dimensional views of a multi-dimensional data set. For your problem, the data matrix consisting of eighteen elements can be considered to exist in eighteen-dimensional space (since this would be the number of dimensions required to simultaneously plot all of the variables against one another). ...