1. Read Delen et al. (2013). Explain how the authors used principal component analysis (PCA) to decompose their data into linear components, similar to discriminant function analysis in MANOVA. How were the factors, eigenvectors, and the covariance matrix determined? What role did the squared error distance play in their conclusions?
2. Continuing with Delen et al. (2013), what role did the Chi-squared automatic interaction detector (CHAID) play in their decision tree algorithm? How does the CHAID technique compare to C5.0, CART (referred to as C&RT by the authors), and the quick, unbiased, efficient statistical tree (QUEST). How do the accuracies of these techniques compare, and how were they measured by the authors?
3. Read Meena et al. (2019). Critique the authors' implantation of the CART algorithm, including their association rules and the set-theoretic justifications they present in Sections 4.4, 4.5, 5, and 6. Do these mathematical bases properly support their computational results presented in section 7? Why or why not?