There are a variety of splines that can be used to fit a regression curve to data ranging from linear splines via cubic and natural splines to smoothing splines. Assume that we have chosen a number of knots K. From the statements below find the one that is not correct in the context of fitting a regression model with splines. Linear and polynomial splines fit models with different coefficients in each region. Natural polynomial splines extrapolate beyond the boundary knots. Smoothing splines are commonly derived from polynomial splines of degree 3 and include a penalty function based on the second derivative. The penalty parameter in smoothing splines controls the smoothness of the solution with small values of the parameter resulting in smoother solutions than larger values of the parameter