Scatter plots are a type of data visualization used to display individual data points in a two-dimensional space. It shows the relationship between two variables or attributes. In scatter plots, each data point is represented by a dot or marker on the graph, and the dot’s position is determined by the values of the two variables being compared.
Scatter plots help in:
- Visualizing data distribution: Scatter plots help understand how data points are distributed in relation to two variables. They can show patterns, trends, clusters, outliers, or the absence of any significant relationships.
- Relationship exploration: Scatter plots are commonly used to explore and visualize the relationship between two continuous variables. There can be positive, negative, or no correlation between the variables.
- Identifying outliers: Outliers are data points that significantly deviate from the general pattern and can be easily identified in a scatter plot. These points are often represented as data points located far from the primary cluster of points.
- Correlation assessment: Scatter plots can help assess the direction and strength of the correlation between two variables. If points are closely packed along a line or curve, it suggests a strong correlation, while a scattered pattern may indicate a weak or no correlation.
- Variable interactions: They help visualize interactions between variables. For example, if one analyzes how temperature and humidity affect plant growth, a scatter plot can show how these two variables interact.
- Data clustering: Scatter plots can sometimes reveal natural groupings or clusters within the data. This is particularly useful in clustering and classification tasks.
Scatter plots are often created using software tools like Excel, Python (using libraries like Matplotlib or Seaborn), or other data visualization platforms. They provide a quick and intuitive way to gain insights into the relationships and distributions within data, making them a valuable tool in data analysis and interpretation.
List of recommended resources #
For a broad overview #
This LabWrite resource page gives the basic difference between line graphs and scatter plots, both of which are used for presenting distribution of data in data visualization and analysis.
This webpage by Stat Trek gives a clear understanding of scatter plots used in data visualization for displaying relationships or distribution of data sets.
For in depth understanding #
Data Visualization: A Practical Introduction
This accessible primer by Kieran Healy explains how to create effective graphics from data. It explains what makes some graphs succeed while others fail and how to think about data visualization in an honest and effective way.
Graphical Methods for Data Analysis
This book by J. M. Chambers provides various old and new graphical methods like scatter plots for analyzing data.
Case study #
This paper uses a scatter plot of the number of exporters, average exporter size, and concentration against both income and income per capita, using country-year level data averaged for the period 2006-2008 to present new data on the micro structure of the export sector for 45 countries and studies how exporter behavior varies with country size and stage of development.
This paper uses scatter plots for the graphical representation of the results obtained for the supply and demand of different countries. Figures 1-6 present scatter plots with the neighborhood access rate in the country in urban areas on the horizontal axis, and the estimates along the econometric method for the proportions of deficit coverage due respectively to demand-side factors, supply-side factors, and combined factors on the vertical axis.
References #
Data Visualization Resources: Types of Charts and Graphs for Data Viz
