Perform PCA on the Iris data. http://www.instantr.com/2012/12/18/performing a-principal-component-analysis-in-r/ To view the dataset, simply type iris at the R prompt. We will not build a regression model for this dataset. Let's just explore the nature of the relationships between the predictor variables. Run PCA (not PCR) on the variables Sepal.Length, Sepal.Width, Petal.Length, and Petal.Width. (a) List the eigenvalues in order from highest to lowest, along with the percentage of variation captured by each principle component. (b) What is the total variation captured by the first component? What is the total variation captured by the first two components? The first three? All four? (c) Make a scree plot. How many principle components do you think are enough to adequately describe the variation in the data? (d) What do the loadings for the components indicate? Be specific. (e) What do the scores for the observations tell you