  This paper focuses on modeling violent crime rates against population over
the years 1960-2014 for the United States via cubic spline based method. We
propose a new min/max algorithm on knots detection and estimation for cubic
spline regression. We employ least squares estimation to find potential
regression coefficients based upon the cubic spline model and the knots chosen
by the min/max algorithm. We then utilize the best subsets regression method to
aid in model selection in which we find the minimum value of the Bayesian
Information Criteria. Finally, we report the $R_{adj}^{2}$ as a measure of
overall goodness-of-fit of our selected model. Among the fifty states and
Washington D.C., we have found 42 out of 51 with $R_{adj}^{2}$ value that was
greater than $90\%$. We also present an overall model for the United States as
a whole. Our method can serve as a unified model for violent crime rate over
future years.
