Statistics
Linear regression & correlation Calculator
Fit paired values, report slope and R², and calculate a specified prediction. This free linear regression & correlation calculator shows the calculation and its assumptions so you can compare your own figures.
Your results
Fit paired values, report slope and R², and calculate a specified prediction.
Calculated outcomes
- Slope
- 2
- Intercept
- 1
- Pearson Correlation
- 1
- Residual Standard Error
- 0
- Observations
- 5
Assumptions and estimates
- Ordinary least squares for paired numeric observations. Constant Y makes correlation undefined. Association does not establish causation; extrapolation and correlated/heteroscedastic errors can make predictions unreliable. No inferential p-values are claimed.
- Method: Ordinary least squares: slope = covariance sum / x deviation sum; intercept = mean y − slope × mean x; R² = squared Pearson correlation.
- Editable inputs are planning assumptions; check the method and result notes for scope.
Useful next steps
- Use Compare options to test an alternative side by side.
- Copy, print or download a record of this calculation when you need one.
Reports open your email app addressed to s@s1.ie with the public page address only. Your inputs are never added.
How the linear regression & correlation calculation works
Ordinary least squares: slope = covariance sum / x deviation sum; intercept = mean y − slope × mean x; R² = squared Pearson correlation.
Editable inputs are planning assumptions; check the method and result notes for scope.
Using this calculator
- Enter your own figures in the labelled inputs. Keep the currencies, units and time periods consistent.
- Choose any applicable rate profile, pattern or assumption shown for this tool. Open additional inputs when relevant.
- Review predicted y and r squared, then read the stated assumptions and eligibility conditions before using the result.
Worked example
This is a static example using illustrative inputs, not facts about you or today’s date. The displayed eligibility selections and assumptions apply only to this example.
See example inputs
- Paired x values
- 1, 2, 3, 4, 5
- Paired y values
- 3, 5, 7, 9, 11
- X value to predict
- 6
- Predicted Y
- 13
- R Squared
- 1