# Eleisha's Segment 48: Principal Component Analysis (PCA)

1. Suppose that only one principal component is large (that is, there is a single dominant value s_i). In terms of the matrix $\displaystyle \mathbf V$ (and anything else relevant), what are the constants $\displaystyle a_j$ and $\displaystyle b_j$ that make a one-dimensional model of the data? This would be a model where $\displaystyle x_{ij} \approx a_j \lambda_i + b_j$ with each of the data points (rows) having its own value of an independent variable $\displaystyle \lambda_i$ and each of the responses (columns) having it's own constants $\displaystyle a_j,b_j$ .
To Think About: 1. Although PCA doesn't require that the data be multivariate normal, it is most meaningful in that case, because the data is then completely defined by its principal components (i.e., covariance matrix) and means. Can you design a test statistic that measures "quality of approximation of a data set by a multivariate normal" in some quantitative way? Try to make your statistic approximately independent of $\displaystyle N$ , the number of data points.