Is the weighted combination of the \(k\) observed variables that accounts for the most variance in the original set of variables. Specifically, the first principal component These derived variables, called principal components, are linear combinations of the observed variables. The goal of PCA is to replace a large number of correlated variables with a smaller number of uncorrelated variables while capturing as much information in the original variables as possible.A useful (text based) tool for looking at the distribution of a numeric vector is the stem function:.(Less frequent values are grouped into an “Other” category.) For factors, summary shows the count of the most frequent values. For numeric values, it shows the minimum, 1st quartile, median, mean, 3rd quartile, and maximum values. :122.11Īs you can see, summary presents information about each variable in the data frame.
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