Enter Regression Data
Use one X,Y pair per line. Commas, spaces, semicolons, or tabs work as separators.
Example Data Table
This sample shows a simple trend you can paste directly into the calculator.
| Observation | Advertising Spend (X) | Sales Units (Y) |
|---|---|---|
| 1 | 1 | 2 |
| 2 | 2 | 4 |
| 3 | 3 | 5 |
| 4 | 4 | 4 |
| 5 | 5 | 5 |
| 6 | 6 | 7 |
| 7 | 7 | 8 |
| 8 | 8 | 9 |
Formula Used
Simple linear regression fits a straight line between one predictor and one response variable. The fitted line minimizes the squared residuals between actual and predicted values.
What the calculator returns:
- Best-fit slope and intercept.
- Predicted Y for your chosen X.
- R², adjusted R², correlation, MAE, MSE, and RMSE.
- Confidence and prediction intervals when enough data exists.
How to Use This Calculator
- Enter X,Y data pairs in the textarea, one pair per line.
- Set the target X value you want to predict.
- Choose a confidence level and decimal precision.
- Optionally rename the X and Y axis labels for the chart.
- Click Calculate Regression Prediction.
- Review the forecast, fit statistics, table, and regression plot.
- Use the CSV or PDF buttons to save the output.
Frequently Asked Questions
1) What does this calculator predict?
It predicts a Y value from a single X value using simple linear regression. The tool also reports the fitted equation, residuals, model fit statistics, and interval estimates.
2) How many data points should I use?
Use at least two points to fit a line, but three or more points are better. More observations usually improve stability and make interval estimates more meaningful.
3) What is the difference between confidence and prediction intervals?
A confidence interval estimates the mean response at a target X. A prediction interval is wider because it estimates a single future observation, including individual variation.
4) What does R² mean here?
R² shows how much of the variation in Y is explained by the fitted straight line. Values closer to 1 usually indicate a stronger linear fit.
5) Can I use negative numbers or decimals?
Yes. The parser accepts negative values and decimal values. Just enter each observation as two numbers separated by a comma, space, semicolon, or tab.
6) Why am I seeing an error about identical X values?
Regression needs variation in X to estimate a slope. If every X value is the same, the denominator becomes zero and the line cannot be fitted.
7) Is this calculator for multiple regression?
No. This page handles one predictor and one response. For multiple regression, you would need a model with several independent variables and matrix-based estimation.
8) Should I trust extrapolated predictions?
Be careful when predicting far outside the observed X range. Extrapolation may look precise mathematically, but it can become unreliable if the linear pattern does not continue.