Exploratory Data Analysis: Kernel Density Estimation - Conceptual Foundations

Recently, I began a series on exploratory data analysis; so far, I have written about computing descriptive statistics and creating box plots in R for a univariate data set with missing values. Today, I will continue this series by introducing the underlying concepts of kernel density estimation, a useful non-parametric technique for visualizing the underlying distribution of a continuous variable. In the second half of this blog post that will be published later here on AnalyticBridge, I will show how to construct kernel density estimates and plot them in R. I will also introduce rug plots and show how they can complement kernel density plots. Before defining kernel density estimation, let's define a kernel. A kernel is a special type of probability density function (PDF) with the added property that it must be even.