ABSTRACT
This chapter discusses the techniques to be used only when a set of discrete data points is available and an approximate functional relationship. It introduces the simplest curve-fitting approach that can be used in problems for which it is necessary to obtain an analytical expression that will approximate a set of discrete data points. The chapter utilizes the control functions only as benchmarks to determine how well the approximating curves match the data. It presents several techniques to curve fit discrete or continuous data. These include algebraic polynomial curve fitting, chebyshev polynomial fitting, and least-squares polynomial fitting, least-squares Chebyshev polynomial fitting, fourier series and spline functions. A brief discussion of the advantages and the disadvantages of the methods are made to aid novice readers in the choice of the appropriate interpolation scheme. The algebraic polynomial technique is the easiest to implement from the existing data.
