The graph of our data appears to have one bend, so let’s try fitting a quadratic linear model using Stat > Fitted Line Plot. It’s very rare to use more than a cubic term. Each increase in the exponent produces one more bend in the curved fitted line. Typically, you choose the model order by the number of bends you need in your line. The most common way to fit curves to the data using linear regression is to include polynomial terms, such as squared or cubed predictors. Here are the data to try it yourself! Fitting Curves with Polynomial Terms in Linear Regression We want to accurately predict the output given the input. For our purposes, we’ll assume that these data come from a low-noise physical process that has a curved function. To compare these methods, I’ll fit models to the somewhat tricky curve in the fitted line plot. How do you fit a curve to your data? Fortunately, Minitab Statistical Software includes a variety of curve-fitting methods in both linear regression and nonlinear regression. This fitted line plot shows the folly of using a line to fit a curved relationship! However, not all data have a linear relationship, and your model must fit the curves present in the data. That is, if you increase the predictor by 1 unit, the response always increases by X units. If your goal is to create a spline (a curve that goes through every data point) or a lowess curve ( shows the general trend with a curve that can be quite jagged), you can jump right to the instructions for that analysis.We often think of a relationship between two variables as a straight line.Jump right to an explanation of interpolation with Prism. If your goal is to simply fit a smooth curve in order to interpolate values from the curve, there is no need to learn much theory.If you already understand the principles of nonlinear regression, and want to see how to fit curves with Prism, jump right to the tutorials.If your goal is to fit a model to your data in order to obtain best-fit values of the parameters, and want to learn the principles first, then read this principles section before trying to fit curves.

The first step is to be clear on what your goal is: This Guide provides a concise introduction to fitting curves, especially nonlinear regression. Yet few statistical texts really explain the principles of curve fitting. Many scientists fit curves more often than the use any other statistical technique.

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