Three ways to plot two numbers against each other and see whether they move together. Start with a plain scatter, add a size dimension with a bubble chart, or add reference lines with a quadrant chart.
A quick look at what's in here. Click through to open any one of them.
A scatter plot templates page like this one is really answering one question: does one measure move with another? Every chart in this family puts a dot for each row of data on two axes (one variable across the bottom, one up the side) and lets you read the relationship at a glance. Height against weight, ad spend against sign-ups, temperature against ice cream sales. If the dots trend upward together you've got a positive relationship; if one goes up as the other goes down, negative; if they're scattered with no pattern, there's probably nothing there worth reporting.
The three templates differ in how much they add to that basic x-y plot. A plain scatter plot handles two variables and stops there: the cleanest option when you just need to show the relationship itself. Add a third variable as dot size with a bubble chart, so you can show, say, GDP against life expectancy with population as the bubble radius, without needing a third axis. A quadrant chart takes the same two-variable layout and drops in a horizontal and vertical reference line (often the average of each axis), splitting the plot into four labeled regions, which is the shape you want when the point isn't the raw correlation but which category each item falls into (high-growth/low-share vs. low-growth/high-share, and so on).
One honest caveat worth stating up front: a scatter plot shows correlation, not causation. Two variables can trend together because one causes the other, because both are driven by a third factor, or by coincidence in a small dataset. PlotSet won't tell you which of those it is. That's still a judgment call for whoever's reading the chart, and a page that pretends otherwise is doing its readers a disservice.
Each one opens its own free maker with sample data already loaded.
Marketing spend against revenue, hours studied against test scores, price against square footage. A plain scatter plot is the fastest way to see if there's a pattern before you go looking for one with statistics.
Country-level data with GDP, life expectancy and population is the classic case. A bubble chart encodes that third number as dot size so you're not forcing a 2D chart to carry three dimensions badly.
Product portfolio by growth rate and market share, or survey results by satisfaction and effort. A quadrant chart's reference lines turn a cloud of dots into labeled buckets: leaders, laggards, and the two groups in between.
One student who aced the test despite minimal study time, one region that spent little but converted a lot. Outliers sit visually apart from the rest of the cloud in any of these three templates, which is often the most useful thing on the chart.
From a spreadsheet to a published chart, step by step.
Two variables: plain scatter plot. Add a size dimension for a third: bubble chart. Split the plot into named regions: quadrant chart.
Each template linked below opens with sample data already bound in, so you can see the exact column layout (x value, y value, and size or category if needed) before uploading your own.
Import a CSV or Excel file, paste from a spreadsheet, or connect a Google Sheet so the chart updates automatically when your source numbers change.
Publish for an interactive version with hover tooltips on every dot, or export a static PNG, SVG or PDF for a deck or report.
Open the editor and start from any template in this category. Sample data is already loaded.
Open the editorUse a plain scatter plot if you only have two numeric variables to compare. Switch to a bubble chart only when you have a genuine third variable to encode as size. Don't add bubble size just for visual interest, since an unnecessary third dimension makes the chart harder to read, not easier.
Free to start with 300+ templates. No credit card required.
