A linear trend is something we are very familiar with, having encountered linear regression for most of this course. Curvilinear regression analysis can be used to determine if not-so-linear trends exist between Y and X.
When we have nonlinear relations, we often assume an intrinsically linear model (one with transformations of the IVs) and then we fit data to the model using polynomial regression. That is, we employ some models that use regression to fit curves instead of straight lines. The technique is known as curvilinear regression analysis. To use curvilinear regression analysis, we test several polynomial regression equations.
So we can say that salary for a programmer increases until age 47 and then decreases.
