## Exploratory Data Analysis: Kernel Density Estimation – Conceptual Foundations

June 9, 2013 33 Comments

*For the sake of brevity, this post has been created from the first half of a previous long post on kernel density estimation. This **first *half focuses *on the conceptual foundations of kernel density estimation*. The *second *half will focus *on constructing kernel density plots and rug plots in R*.

#### Introduction

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 follow-up post, 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.

**But first – read the rest of this post to learn the conceptual foundations of kernel density estimation.
**

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