About OLS Linear Regression & ANOVA Calculator
Professional Ordinary Least Squares (OLS) linear regression modeler and diagnostic workbench. Generates slope and intercept parameters with standard errors, t-statistics, p-values, R², adjusted R², standard error of estimate (Se), complete ANOVA table, and predictions with 95% confidence intervals.
Key Capabilities & Features
- Exact Ordinary Least Squares fitting: computes slope (β₁) and intercept (β₀) with formatted equation
- Parameter significance: Standard errors, t-ratios, and two-tailed p-values for both parameters
- Goodness of fit: R², Adjusted R² (penalizing model complexity), and standard error of estimate (Se)
- Complete ANOVA decomposition: SS Regression, SS Residual, SS Total, Mean Squares, and F-statistic
- Interactive predictor module: Enter arbitrary X values to predict Ŷ with 95% confidence and prediction intervals
How to Use OLS Linear Regression & ANOVA Calculator
Enter Predictor X & Response Y
Input paired numerical data into the predictor and response fields.
Review Model Equation
Read the fitted regression equation (y = mx + b) and R² goodness of fit.
Examine ANOVA Table
Inspect degrees of freedom, mean squares, F-statistic, and regression significance.
Predict Future Values
Enter any target X in the predictor box to compute Ŷ and confidence bounds.
Privacy & In-Browser Execution Guarantee
100% Client-Side. Numerical regression matrix algebra and ANOVA partitioning compute locally.
Frequently Asked Questions
What is the difference between confidence interval and prediction interval?
A confidence interval reflects uncertainty around the average response for a given X, whereas a prediction interval estimates the wider range in which an individual new observation will fall.
What does the F-statistic in the ANOVA table test?
The F-test assesses the null hypothesis that all regression coefficients are zero, determining whether the model explains significantly more variance than chance.