Curve fitting means finding a mathematical function or plot curve that best fits a set of data. By doing so, you can see patterns in a data set, predict future data points, and understand the relationship between different factors.
Mathcad has a number of specialized curve-fitting functions.
A least squares fit is the method most commonly used to find the line that best fits a set of data. “Least squares” refers to the sum of the squares of the distances from the individual data points to the line itself. A least squares fit finds the smallest possible sum, or closest fit.
Mathcad has three built-in functions to fit data to a line: line, slope, and intercept.
Beyond that Mathcad has many built-in curve fitting functions for the most common model functions: exponential, logistic, logarithmic, sinusoidal, and power.
In addition, you can use one of the generalized curve fitting functions to specify your own model function.
Further, you can analyze your data to evaluate the quality of the fit, using other data analysis functions. The goal of all the curve fitting functions is to enable you to best understand your data and to control factors that influence the data.