The document discusses the least squares method and cubic fitting method. [1] It explains that least squares finds the best fit curve to a set of data points by minimizing the sum of the squared residuals. [2] Cubic fitting finds the smoothest curve that exactly fits the data points using a cubic polynomial function. [3] An example applies the cubic fitting method to bacterial growth data to determine the parameters for the best fitting cubic curve.