A scatterplot is a powerful and straightforward tool in data visualization that allows you to explore relationships between two variables. It represents data points on a two-dimensional graph, where the x-axis and y-axis correspond to the two variables being compared. This graphical technique is commonly used in statistics to visualize potential correlations, patterns, or trends in a dataset.
One of the scatterplot’s primary advantages is its ability to reveal both linear and non-linear relationships between variables. For example, a positive relationship will show an upward trend, while a negative one will indicate a downward slope. If no correlation exists, the points will be scattered randomly across the plot. This flexibility makes scatterplots ideal for data exploration, especially when trying to understand the underlying dynamics in business analytics, economics, or social sciences.
Scatterplots are frequently used alongside regression lines, which add an extra layer of analysis. A trendline or line of best fit can be included to approximate the general direction of the data, allowing for easier interpretation of the relationships at play.
Additionally, scatterplots can incorporate more complexity, such as using color coding or different shapes to represent categories within the data, turning a simple two-dimensional plot into a multivariate analysis tool. This is particularly useful in exploratory data analysis, as it helps identify subgroups or clusters within the data.
Though simple, scatterplots are great for turning complex data into easy-to-understand visuals. When used well, they quickly show key relationships and offer a clear, helpful summary of the data.
List of recommended resources #
For a broad overview #
This video tutorial by Khan Academy, part of College Statistics lesson, provides an overview on how to construct a scatter plot, how to describe scatterplots, as well as the use of clusters and outliers in scatterplots.
This entry by JMP Statistical Discovery provides a broad overview of scatterplots, how they are used along with various examples. It also talks about scatter plot matrix, and the different types of data represented via scatterplots.
For in depth understanding #
A complete guide to scatter plots
This article by Mike Yi for Atlassian provides an in-depth understanding of scatter plots, when one should use a scatter plot, as well as some common issues when using scatter plots.
Scatter Plots: Understanding and Using Scatter Plots
This blog by Tableau provides a detailed description of how to read scatter plots, what type of analysis scatter plots support, when and how to use scatter plots for visual analysis. It also provides good and bad examples of scatter plots for better understanding of learners.
Case study #
Relative status of journal and conference publications in Computer Science
This article aims to quantify the relative importance of CS journal and conference papers, showing that CS papers in leading conferences match the impact of papers in mid-ranking journals. The article also uses a scatter plot showing the correlation of the Google Scholar Impact Factor and the ISI Impact Factor.
This study by Ali Akbar Jamali & Reza Ghorbani Kalkhajeh simulates and predicts the urban environment growth in Tehran using the remote sensing data, multi-layer perceptron neural network, zonal, trend, and profile modeling. After building the probability map of the land changes, random points scatter and kernel analysis (RPSKA) was used. The pixel values of all the maps were extracted to the random points for the scatter plot and kernel analysis.
References #
