How to Read Scatter Chart and Bubble Chart

Scatter charts show the relationships among the numeric values in several data series, or between two groups of numbers as one series of XY coordinates.

A scatter chart has two value axes, showing one set of numerical data along the horizontal axis (X-axis) and another one along the vertical axis (Y axis). It combines these values into single data points and displays them in uneven intervals, or clusters. Scatter charts are commonly used for displaying and comparing numeric values, such as scientific, statistical and engineering data.

Scatter plots are best used for data sets in which there is likely to be some form of relationship or association between two different elements included within the data, for example, the relationship between Sales and Quantity per Customer. Financial analysts often use these plots to show correlation between two funds, two stocks or even a stock and the general market.

Example

The following chart presents the relationship between salary and age for 474 employees of a company. This type of data would be expected to show some form of trend since, as the staff gains experience, you would expect their value to the company to increase and therefore their salary to also increase.

As an example, one of the employees as 28.5 years old and had a salary of $16,080. To put this data onto a scatter plot, we insert age into the horizontal axis and salary onto the vertical axis. The different entries onto the plot are the 474 combinations of age and salary resulting from a selection of 474 employees, with each observation being a single point on the chart.

The charts shows that in fact for this company there is no obvious relation between salary and age. We can observe that the age range of employees is from 23 to 65. We can also see that a lone individual earns a considerably higher salary than all the others and that starters and those nearing retirement are actually on similar salaries.

Interpretting a Scatter Plot

Having drawn the plot it is necessary to interpret it. The author should do this before it is passed to any user. The most obvious relationship between the variables X and Y would be a straight line or a linear one. If such a relationship can be clearly demonstrated then it will be of assistance to the reader if this is shown explicitly on the scatter plot. This procedure is known as linear regression and we will discuss it in another course.

Bubble Chart

A Bubble Chart or Bubble Graph is used to display three dimensions of the data. Each datapoint is plotted as a disk that expresses two of the values through the disk's xy location and the third through its size. Bubble charts can facilitate the understanding of social, economical, medical, and other scientific relationships. A Bubble Chart can be plotted on both Cartesian coordinate grid and Scatter coordinate grid.

The following chart shows the Gross Domestic Product and Gross National Income (per Capita) of various countries. The size of the bubble shows the actual GDP of the place.

Related Downloads

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.