PC - seems to account for diets in coastal regions or...

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Principal Components Analysis For example, suppose you are interested in examining the relationship among measures of food consumption from different sources. The sample data set Protein records the amount of protein consumed from nine food groups for each of 25 European countries. The nine food groups are red meat (RedMt), white meat (WhiteMt), eggs (Eggs), milk (Milk), fish (Fish), cereal ( Cereal), starch (Starch), nuts (Nuts), and fruits and vegetables (FruVeg).
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The table of eigenvectors in reveals that the first eigenvector has equally large loadings on all of the animal-protein variables. This suggests that the first component is primarily a measure of animal-protein consumption. This eigenvector also has a large loading on the variable Starch and negative loadings on the variables Cereal and Nuts. The second eigenvector has high positive loadings on the variables Fish, Starch, and FruVeg. This component
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Unformatted text preview: seems to account for diets in coastal regions or warmer climates. The remaining components are not as easily identified. In previous fugure, each vector corresponds to one of the analysis variables and is proportional to its component loading. For example, the variables Eggs, Milk, and RedMt all load heavily on the first component. The variables Fish and FruVeg load heavily on the second component but load very little on the first component. The information provided by the variable Country reveals that western European countries tend to consume protein from more expensive sources (that is, meat, eggs, and milk), while countries near the Mediterranean Sea rely more heavily on fruits, vegetables, nuts, and fish for their protein sources. Eastern European countries rely more on cereal crops and nuts to supply their protein....
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PC - seems to account for diets in coastal regions or...

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