The straight line relationship will determine the slope and intercept on the Y axis. The equation of the straight line then determines the unknown values. It can find unknown values within the data range and also extrapolate values outwith the data range.

When the relationship is precise, such as machinery rotating at a constant acceleration, interpolation may provide the most accurate results. Regression is a more useful mathematical method for finding the relationship for dependent scattered variables that are not precise. The line of best fit or trend line attempts to produce a fit closest to points within the experimental data.

These data variables often don’t fit within a straight line and can fit better to a curve. Polynomial, Lagrange, or Spline interpolation can sometimes achieve the best fit to the data. Alternatively, to obtain the best fit requires the use of non-linear regression methods. There are different types of non-linear regression curves, including logarithmic, exponential, power, polynomial, and saturation growth curves. To find the best fit, it may be necessary to try different methods to determine the closest fit. The coefficient of determination R² frequently determines how close the predictions are to real data points. However, to obtain a perfect fit to real data points, the R² value must be equal to 1.