The 2D Kernel Density plot is a smoothed color density representation of the scatterplot, based on kernel density estimation, a nonparametric technique for probability density functions. The goal of density estimation is to take a finite sample of data and to infer the underyling probability density function everywhere, including where no data point are presented. In kernel density estimation, the contribution of each data point is smoothed out from a single point into a region of vicinity. These smoothed density plot shows an average trend for the scatter plot.
Origin supports two methods to calculate the density plot Bivariate Kernel Density Estimator and Rules of Thumb. You can refer to ks2density function for more details about the algorithm.
To create a 2D Kernel Density plot:
Specify the input data.
Density Estimation data
This determines where the calculated data for the graph is stored.
This determines where the data of the displayed scatter plot is stored. Only available when Number of Points to Display is not 0.