Abstract
This paper presents a method for fitting the quadratic curve which has the least-squares distance from two variable data. Fitting of smooth curves is one of the most important themes in pattern recognition and data analysis. Simple, multiple or multivariate regression analyses are in use for a data set consists of observations on some variables which can be classified into response and explanatory ones. But, there are few analyses that work well for a data set whose variables can not be distinguished between response and explanatory. For such data set, a straight line, which has least sum of square distances from data points, is obtained by using principal component analysis. We extend the principal component analysis to the fitting of quadratic curve and show performance of the proposed method by a numerical example.
