  The procedure of Least Square-Errors curve fitting is extensively used in
many computer applications for fitting a polynomial curve of a given degree to
approximate a set of data. Although various methodologies exist to carry out
curve fitting on data, most of them have shortcomings with respect to
efficiency especially where huge datasets are involved. This paper proposes and
analyzes a matricized approach to the Least Square-Errors curve fitting with
the primary objective of parallelizing the whole algorithm so that high
performance efficiency can be achieved when algorithmic execution takes place
on colossal datasets.
