Prepare your dataset
Collect paired observations for Y and X, clean outliers, and ensure numeric columns.
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Browser-based regression analysis
Linear Regression Calculator
Fit a straight line, compute slope and intercept, and understand the strength of the relationship between two variables without leaving your browser.
- Upload spreadsheet data or paste values directly into an editable table.
- Generate regression coefficients, R-squared, and residual diagnostics instantly.
- Visualize the fitted line with an interactive scatter plot for presentations and reports.
Save regression projects
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Data table
Import a CSV or edit values directly. The first column is treated as X and the second as Y.
Linear regression dataset
Enter paired X and Y values, paste from a spreadsheet, or import a CSV to get started.
No values yet
Add at least two rows of paired X and Y numbers to run the regression.
Linear regression summary
Choose a demo dataset or analyze your own CSV. Click calculate to update the metrics and visualization.
Study Hours vs Exam Score
Ten students recorded how many hours they studied for an exam and the score they achieved. The relationship is close to linear and shows a clear positive trend.
Slope
8.30303
Intercept
46.563636
R²
0.9901
Pearson r
0.995
Data points
10
Mean X
3.75
Mean Y
77.7
Regression equation
ŷ = 8.30303x + 46.563636
Standard error of estimate: 1.3326
Upload CSV files up to 10MB. Extra columns beyond the first two are ignored during calculation.
Scatter plot with best-fit line
Residual diagnostics
Residuals highlight the difference between actual values and the fitted regression line. Large positive residuals mean the model underestimates the observed value, while negative residuals indicate an overestimate.
| Study Hours | Exam Score | Predicted Y | Residual |
|---|---|---|---|
| 1.5 | 58 | 59.0182 | -1.018 |
| 2 | 63 | 63.1697 | -0.17 |
| 2.5 | 67 | 67.3212 | -0.321 |
| 3 | 71 | 71.4727 | -0.473 |
| 3.5 | 76 | 75.6242 | 0.376 |
| 4 | 82 | 79.7758 | 2.224 |
| 4.5 | 86 | 83.9273 | 2.073 |
| 5 | 88 | 88.0788 | -0.079 |
| 5.5 | 91 | 92.2303 | -1.23 |
| 6 | 95 | 96.3818 | -1.382 |
How the calculator works
Linear regression fits the best possible straight line through paired observations by minimizing squared residuals.
Load data into the editor
Paste values, upload CSV, or use a demo dataset to start quickly.
Run regression and review outputs
Compute slope, intercept, R², and residuals, then inspect charts for model fit.
Interpret regression output with confidence
Go beyond slope and intercept using residual diagnostics and chart overlays.
Check linearity before trusting the model
If data curves or fans out, consider transformations or alternative models.
Center and scale when appropriate
Standardization can improve numerical stability and interpretability.
Communicate uncertainty
High R² still allows prediction error—report residual insights and outliers.
Popular use cases
Apply linear regression across education, marketing, and operations.
Forecast academic performance
Relate study behavior to exam outcomes to identify support opportunities.
Optimize marketing spend
Compare ad budget with lead volume to find efficient spend levels.
Improve operational planning
Map input resources to output for better scheduling and waste reduction.
Frequently asked questions
Learn how to prepare data and interpret common regression outputs.
Can I upload my own CSV data?
Yes. Upload CSV up to 10MB; the first two columns are used for regression.
How do I interpret slope and intercept?
Slope is change in Y per unit X; intercept is expected Y when X is zero.
What is a good R-squared value?
It depends on context. Higher is better, but always validate with residuals.
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