  To deal with non-linear relations between the predictors and the response, we
can use transformations to make the data look linear or approximately linear.
In practice, however, transformation methods may be ineffective, and it may be
more efficient to use flexible regression techniques that can automatically
handle nonlinear behavior. One such method is the Polynomial Spline (PS)
regression. Because the number of possible spline regression models is many,
efficient strategies for choosing the best one are required. This study
investigates the different spline regression models (Polynomial Spline based on
Truncated Power, B-spline, and P-Spline) in theoretical and practical ways. We
focus on the fundamental concepts as the spline regression is theoretically
rich. In particular, we focus on the prediction using cross-validation (CV)
rather than interpretation, as polynomial splines are challenging to interpret.
We compare different PS models based on a real data set and conclude that the
P-spline model is the best.
