Exploratory Data Analysis: Quantile-Quantile Plots for New York’s Ozone Pollution Data

Introduction

Continuing my recent series on exploratory data analysis, today’s post focuses on quantile-quantile (Q-Q) plots, which are very useful plots for assessing how closely a data set fits a particular distribution.  I will discuss how Q-Q plots are constructed and use Q-Q plots to assess the distribution of the “Ozone” data from the built-in “airquality” data set in R.

Previous posts in this series on EDA include

• Descriptive statistics
• Box plots
• The conceptual foundations of kernel density estimation
• How to construct kernel density plots and rug plots in R
• Violin plots
• The conceptual foundations of empirical cumulative distribution functions (CDFs)
• 2 ways of plotting empirical CDFs in R
• Conceptual foundations of histograms and how to plot them in R
• Combining histograms and density plots in R
• The 5-number summary: Differences between fivenum() and summary() in R
• Useful R Functions for Exploring a Data Frame

Learn how to create a quantile-quantile plot like this one with R code in the rest of this blog!