Final answer:
The principle component analysis converts original data into uncorrelated synthetic variables, retaining most of the original variation. It includes identifying independent and dependent variables and conducting regression analysis to find the line of best fit and correlation coefficient.
Step-by-step explanation:
The principle component (or factor) analysis is a statistical technique primarily used in data reduction. It involves converting the original data into a new set of variables, known as principal components, which are uncorrelated and which are ordered such that the first few retain most of the variation present in all of the original variables. The correct answer to the student's question is a) Converts the original data to a set of synthetic variables. This process involves identifying the independent and dependent variables, drawing a scatter plot, and using regression analysis to find the line of best fit and the correlation coefficient. The significance of the correlation coefficient helps interpret the strength and direction of the linear relationship between variables.