There are 39 observations representing the 39 Peruvians in the dataset and 10 variables. Principal Component Analysis was performed on the data set, giving the following result based on the correlation matrix. If the rule that only eigenvalues at least as large as 1 will be counted, then only (how many? type a whole number answer) components will be considered and about of the variation is explained by their eigenvalues together. Component eValue 1 3.449237 2 1.692153 3 1.543212 Total 4 0.856276 5 0.783724 6 0.606369 7 0.438018 8 0.328671 9 0.205767 10 0.096575 (answer to 2 decimal places with % sign e.g.49.85%) 10

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There are 39 observations representing the 39 Peruvians in the dataset and 10 variables.
Principal Component Analysis was performed on the data set, giving the following result based
on the correlation matrix. If the rule that only eigenvalues at least as large as 1 will be counted,
then only (how many? type a whole number answer)
components will be
considered and about
of the variation is explained by their eigenvalues together.
Component eValue
1 3.449237
2 1.692153
3 1.543212
4 0.856276
5 0.783724
6 0.606369
7 0.438018
8 0.328671
9 0.205767
10 0.096575
Total
(answer to 2 decimal places with % sign e.g. 49.85%)
10
Transcribed Image Text:There are 39 observations representing the 39 Peruvians in the dataset and 10 variables. Principal Component Analysis was performed on the data set, giving the following result based on the correlation matrix. If the rule that only eigenvalues at least as large as 1 will be counted, then only (how many? type a whole number answer) components will be considered and about of the variation is explained by their eigenvalues together. Component eValue 1 3.449237 2 1.692153 3 1.543212 4 0.856276 5 0.783724 6 0.606369 7 0.438018 8 0.328671 9 0.205767 10 0.096575 Total (answer to 2 decimal places with % sign e.g. 49.85%) 10
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