Multiple Series Scatter Plot Maker
Compare multiple data sets in a single scatter plot - add series, customize colors, and export in any format.
Series Summary
Export Chart
Key Features
How to Use
Pro Tips
Data Input
Use spaces or commas to separate values. Invalid numbers are automatically filtered out.
Series Management
Toggle series visibility to compare specific datasets. Use duplicate to create variations.
Export Quality
Use SVG for scalable vector graphics, PNG for transparency, JPEG for smaller file sizes.
When Two Series Aren't Enough
Most charting tools quietly assume you're comparing one or two groups. Real datasets rarely cooperate. Sales by region might span six territories. A clinical study might track five cohorts. A clustering output can leave you with a dozen labelled groups. The multi-series scatter plot maker is built for those moments - when every group needs to sit on the same axes and you'd rather not flip between separate charts to see the pattern.
The hard part is keeping it readable
More series means more visual noise. The first decision to get right is colour. Adjacent shades from the same family - light blue and slightly darker blue, two near-identical greens - start blending the moment you have four or more series at small marker sizes. The default palette here is ordered so the first six colours read as distinct even when printed in black and white. Past that, lean on bold, separated hues, and consider hiding the less important series with the visibility toggle instead of forcing every group on screen at once.
Trendlines: use them sparingly
Each series has its own trendline checkbox. It's tempting to enable all of them. Don't. Ten regression lines on a single chart turn the whole thing into a bowl of spaghetti. A pattern that tends to work: turn trendlines on for the two or three groups whose direction matters to the argument you're making, and leave the rest as plain dots. The eye will follow the lines and treat the unmarked series as supporting evidence - which is usually how you want the reader to look at the chart anyway.
Where multi-series scatter plots actually shine
Some honest examples from the kinds of charts people build with this tool: marketing teams plotting ad spend against revenue across acquisition channels, biology students charting gene expression against treatment dose for several cell lines, financial analysts mapping risk versus return for different asset classes, product managers comparing user retention against onboarding length across signup cohorts, and physics labs overlaying experimental runs against the same model curve. The unifying thread is that the comparison across more than two groups is the insight - not any single series on its own.
Layout, legend placement, and density
The legend on the bottom is fine for two or three series. Once you cross five, the right-side legend almost always reads more cleanly because each series gets its own line of text rather than wrapping awkwardly across the chart width. Marker size matters more than people expect, too - at six pixels, points overlap and bleed. Drop to four pixels for dense charts, push to eight or ten when you only have a handful of points per series. Subtle changes here often do more for readability than tweaking colours.
Exporting without losing detail
Scatter charts are unusually sensitive to export format because each marker is a tiny shape - JPEG compression can visibly soften the edges of your dots in ways that aren't noticeable on bar or line charts. PNG is a safe default for slides and the web. SVG is the right choice when the chart is going into a printed report, an academic paper, or anything that may be resized later: vector graphics stay pixel-sharp at any size. JPG is fine when file size is the hard constraint, but check the markers afterwards rather than trusting that everything came out clean.
Browser-only by design
Everything happens in your browser. The numbers you paste, the colours you pick, the chart you generate - none of it leaves the page. There's no upload step, no signup, no dataset saved on a server somewhere. That's the point. Half the reason people reach for a quick online plot maker instead of a Python notebook is that they don't want to think about where their data is going. So we made sure it's not going anywhere.
Frequently Asked Questions About the Scatter Plot Maker & Calculator
How do I create a scatter plot with a regression line?+
Paste your X and Y values into the Data Entry panel - comma-separated, space-separated, or copied straight from Excel all work. Toggle Show Trendline in the Trendline section, and the scatter plot maker instantly draws the line of best fit and surfaces the regression equation y = mx + b along with slope, y-intercept, R², the correlation coefficient r, and RMSE in the Regression Analysis panel.
How do I find the regression equation from a data table?+
Enter the X column and the Y column from your data table into the two input fields. The scatter plot calculator computes the slope and y-intercept using the least-squares method and displays the regression equation in the form y = mx + b. The numerical answer updates in real time as you edit the data - no manual computation, and no spreadsheet formulas required.
What does R-squared mean and how do I interpret it?+
R² (the coefficient of determination) is the proportion of the variation in Y that is explained by X under the linear model. R² = 0.85 means 85% of the variation in Y can be predicted from X, with the remaining 15% attributable to other factors or noise. As a rough guide: R² above 0.7 is a strong fit, 0.4–0.7 is moderate, and below 0.4 means the linear trend may be unreliable for prediction.
What is the correlation coefficient (r) and how is it different from R²?+
The correlation coefficient r measures both the strength and direction of the linear relationship between X and Y, ranging from −1 (perfect negative) through 0 (no linear relationship) to +1 (perfect positive). R² is simply r squared, so it discards the sign and only conveys strength. Use r to describe direction, R² to describe explanatory power.
What are residuals and why are they useful?+
A residual is the vertical distance between an observed Y value and the value the regression line predicts for that X. Residuals reveal whether your linear model is appropriate: if residuals are randomly scattered around zero, the linear fit is sound. If they form a curve, fan out, or cluster, a non-linear model - or a transformation - is likely a better choice. Toggle Show Residual Plot above to inspect them visually.
How does the calculator detect and highlight outliers?+
When Highlight Outliers is enabled, the scatter plot calculator standardizes each residual (subtract the mean residual, divide by the residual standard deviation) and flags any point whose standardized residual exceeds ±2. Flagged points render in red so you can investigate them - they may indicate data-entry typos, unusual cases, or genuine anomalies that you may want to exclude before re-running the regression.
Can the calculator handle non-linear or multiple regression?+
This tool is designed for simple linear regression with one independent variable (X) and one dependent variable (Y). Polynomial, exponential, logarithmic, and multiple-regression models are not currently supported. For non-linear data, you can sometimes apply a transformation (e.g. log Y) and fit a linear model in the transformed space.
How do I enter data into the scatter plot maker?+
Type your X values into the X Values field and your Y values into the Y Values field. Both comma-separated (e.g. 1, 2, 3) and space-separated (e.g. 1 2 3) formats are accepted, and you can paste directly from Excel or Google Sheets - the parser handles tabs and newlines too. The data preview table underneath the inputs shows each parsed pair so you can verify alignment before plotting.
What file formats can I download my scatter plot in?+
Four formats are supported: PNG (lossless raster, best for slides and web), JPEG and JPG (smaller raster files, good for emailing), and SVG (scalable vector format, perfect for print, LaTeX, and large displays). SVG is recommended whenever you need to scale the chart up without quality loss.
Is my data uploaded to a server?+
No. All computation - plotting, the regression equation, R², residuals, and outlier detection - happens locally in your browser using JavaScript. Your data is never transmitted, stored, or logged. That's also why no sign-up is required: there's nothing for us to store on your behalf.
Is the scatter plot maker and calculator free?+
Yes. The scatter plot maker and calculator is 100% free, browser-based, and unrestricted - no sign-up, no watermark, no usage caps, and no paid tier. Export as many charts as you need, in any of the four supported formats.
Can I customize the appearance of my scatter plot?+
Extensively. The Style and Series & Color sections let you change marker color and size, background color, text color, trendline color, and legend position. The Animation and Grid sections control hover effects, gridlines, tooltip theming, and animation speed. You can produce a chart that matches your brand, journal style guide, or presentation theme.