Upload CSV/Excel files or enter data manually to create beautiful scatter plots. Map your columns instantly and customize your chart.
Copy these templates to create your own CSV files for testing:
X,Y,Series 10,12,Dataset A 15,18,Dataset A 20,25,Dataset A 25,30,Dataset A 12,22,Dataset B 18,28,Dataset B 22,35,Dataset B 28,40,Dataset B
Temperature,Pressure,Experiment 23.5,101.3,Trial 1 25.2,102.1,Trial 1 27.8,103.5,Trial 1 24.1,98.7,Trial 2 26.3,99.9,Trial 2 28.9,101.2,Trial 2
Scatter plots are one of the most practical ways to visualize relationships between two sets of numerical data. Whether you are analyzing business metrics like marketing spend versus revenue, or plotting scientific measurements, this tool lets you quickly turn raw spreadsheet data into a clear chart without needing heavy software.
To get the best results, your Excel (.xlsx, .xls) or CSV file needs to be structured correctly. Here are the basic rules for formatting your data before uploading:
A scatter chart is ideal when you want to look for correlations. For instance, if you suspect that higher temperatures lead to more ice cream sales, plotting temperature on the X-axis and sales on the Y-axis will reveal if there is a distinct upward trend.
It is also highly useful for spotting outliers. If most of your data points cluster together in a specific area, any point that falls far outside that cluster might indicate an error in data collection or a unique event worth looking into.
When working with sensitive business or research data, privacy is a priority. This Excel to Scatter Plot maker processes all your data locally right in your web browser. Your spreadsheet files are never uploaded to our servers, ensuring your information remains entirely private and secure on your device.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.