Blog Archives

Exploratory Data Analysis: Conceptual Foundations of Histograms – Illustrated with New York’s Ozone Pollution Data

Exploratory Data Analysis: Conceptual Foundations of Histograms – Illustrated with New York’s Ozone Pollution Data

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

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Exploratory Data Analysis – Kernel Density Estimation and Rug Plots on Ozone Data in New York and Ozonopolis

Exploratory Data Analysis – Kernel Density Estimation and Rug Plots on Ozone Data in New York and Ozonopolis

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

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Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R

Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R

Introduction Continuing my recent series on exploratory data analysis (EDA), and following up on the last post on the conceptual foundations of empirical cumulative distribution functions (CDFs), this post shows how to plot them in R.  (Previous posts in this series on EDA include descriptive statistics, box plots, kernel density estimation, and violin plots.) I

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Exploratory Data Analysis: Conceptual Foundations of Empirical Cumulative Distribution Functions

Exploratory Data Analysis: Conceptual Foundations of Empirical Cumulative Distribution Functions

Introduction Continuing my recent series on exploratory data analysis (EDA), this post focuses on the conceptual foundations of empirical cumulative distribution functions (CDFs); in a separate post, I will show how to plot them in R.  (Previous posts in this series include descriptive statistics, box plots, kernel density estimation, and violin plots.) To give you

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Exploratory Data Analysis: Combining Box Plots and Kernel Density Plots into Violin Plots for Ozone Pollution Data

Exploratory Data Analysis: Combining Box Plots and Kernel Density Plots into Violin Plots for Ozone Pollution Data

Introduction Recently, I began a series on exploratory data analysis (EDA), and I have written about descriptive statistics, box plots, and kernel density plots so far.  As previously mentioned in my post on box plots, there is a way to combine box plots and kernel density plots.  This combination results in violin plots, and I

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Exploratory Data Analysis: Kernel Density Estimation in R on Ozone Pollution Data in New York and Ozonopolis

Exploratory Data Analysis: Kernel Density Estimation in R on Ozone Pollution Data in New York and Ozonopolis

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 analyzing the same data set with kernel density estimation, a useful non-parametric technique for visualizing

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Exploratory Data Analysis: Variations of Box Plots in R for Ozone Concentrations in New York City and Ozonopolis

Exploratory Data Analysis: Variations of Box Plots in R for Ozone Concentrations in New York City and Ozonopolis

Introduction Last week, I wrote the first post in a series on exploratory data analysis (EDA).  I began by calculating summary statistics on a univariate data set of ozone concentration in New York City in the built-in data set “airquality” in R.  In particular, I talked about how to calculate those statistics when the data

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When Does the Kinetic Theory of Gases Fail? Examining its Postulates with Assistance from Simple Linear Regression in R

When Does the Kinetic Theory of Gases Fail?  Examining its Postulates with Assistance from Simple Linear Regression in R

Introduction The Ideal Gas Law, , is a very simple yet useful relationship that describes the behaviours of many gases pretty well in many situations.  It is “Ideal” because it makes some assumptions about gas particles that make the math and the physics easy to work with; in fact, the simplicity that arises from these

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Exploratory Data Analysis – Computing Descriptive Statistics in R for Data on Ozone Pollution in New York City

Exploratory Data Analysis – Computing Descriptive Statistics in R for Data on Ozone Pollution in New York City

Introduction This is the first of a series of posts on exploratory data analysis (EDA).  This post will calculate the common summary statistics of a univariate continuous data set – the data on ozone pollution in New York City that is part of the built-in “airquality” data set in R.  This is a particularly good data set

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How to Calculate a Partial Correlation Coefficient in R: An Example with Oxidizing Ammonia to Make Nitric Acid

How to Calculate a Partial Correlation Coefficient in R: An Example with Oxidizing Ammonia to Make Nitric Acid

Introduction Today, I will talk about the math behind calculating partial correlation and illustrate the computation in R with an example involving the oxidation of ammonia to make nitric acid using a built-in data set in R called stackloss.  In a separate post, I will also share an R function that I wrote to estimate partial correlation.

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