Correlation question
- Input
- "Altura e peso estão relacionados?"
- Expected output
- r ≈ 0.78 (forte)
Correlation answers the question without a predictive equation.
difference between correlation and linear regression
Correlation vs Linear Regression — Which to Use?
Correlation quantifies the strength of the linear association (r). Regression goes further: it estimates Ŷ = β₀ + β₁X to predict Y values from X.
Practical differences
- Use correlation when you want to know IF and HOW MUCH variables are related. Use regression when you want to PREDICT Y from X or understand the unit effect of X on Y.
- Correlation is symmetric: r(X,Y) = r(Y,X). Regression is not: the line of Y on X ≠ the line of X on Y.
Examples
Regression question
- Input
- "Qual o peso esperado para 175cm?"
- Expected output
- Ŷ = β₀ + β₁ × 175
Regression provides a quantitative prediction.
Full tool FAQ
Pearson's r measures the strength and direction of the linear relationship between two variables. It ranges from −1 to +1: values near ±1 indicate strong correlation; near 0 indicate weak or absent correlation.
Frequently asked questions
Is r² in correlation and r² in regression the same?
In simple linear regression, yes: the regression R² equals the square of the Pearson r coefficient numerically.
Does this page replace official or professional review?
No. It helps explain the scenario and use the tool more safely, but real decisions should consider official sources, full context and qualified guidance when needed.