ABSTRACT
This chapter covers several of the most common methods for interpolation. There is rarely enough data, but it often takes a lot of time, money, and effort to get even a single data point. However, one would like to have some representation of the measured system in the voids between data points. The process of artificially filling such voids is called data interpolation. A good interpolation routine should be robust to the addition of new data points, that is, new data points should modify the interpolation curve at non-adjacent intervals as little as possible. Ideally, there should be no change at all. One more method that produces a nice, smooth interpolation curve and is relatively immune to the addition of new data points is called the cubic spline interpolation method. Extrapolation is the process of filling voids outside of a measured region. The MATLAB built-ins allow us to send an additional argument to obtain the extrapolated points.
