![]() 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. ![]() ![]() We often think of a relationship between two variables as a straight line.
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