Regression Calculator

Perform linear regression analysis with slope, intercept, and R-squared.

R-Squared

0.9985

Predicted Y

11.89

Regression Results

Slope1.9700
Intercept0.0700
Equationy = 1.97x + 0.07
r0.9992
R-Squared0.9985

Use the Regression Calculator above to calculate your results. Enter your values and see instant results — all calculations run in your browser.

Disclaimer: This calculator is for informational purposes only and does not constitute tax, financial, or legal advice. Results are estimates based on the information you provide and current rates. Always consult a qualified tax professional or financial advisor for advice specific to your situation.

How It Works

Our Regression Calculator provides instant linear regression analysis, crucial for forecasting trends and making data-driven decisions. In 2026, understanding the correlation between marketing spend and sales, or interest rates and loan defaults, is vital for businesses aiming for a competitive edge and optimized resource allocation.

This calculator utilizes the least squares method to determine the line of best fit. It minimizes the sum of the squared residuals, calculating the slope (b) as Cov(x,y) / Var(x) and the intercept (a) as mean(y) - b * mean(x), with R-squared (R²) indicating the proportion of variance in the dependent variable predictable from the independent variable.

Always check for outliers, as they can significantly skew your regression line and R² value. Remember that correlation does not imply causation; a strong R² suggests a relationship, but not necessarily that one variable directly causes the other. Ensure your data set is sufficiently large and diverse for meaningful results.

Example: Predicting 2026 Q3 Sales Based on Q2 Marketing Spend

  1. 1 Input Q2 2026 marketing spend (X) and corresponding sales (Y) for the past 5 quarters: (50000, 1200000), (65000, 1450000), (70000, 1550000), (55000, 1300000), (80000, 1700000).
  2. 2 The calculator processes these data points to find the linear relationship: Slope (b) = 17.5, Intercept (a) = 325000, R-squared (R²) = 0.96.
  3. 3 Assuming a Q2 2026 marketing spend of $75,000, the predicted sales (Y) would be: Y = 325000 + 17.5 * 75000.
  4. 4 The predicted sales for Q3 2026, based on a $75,000 marketing spend in Q2 2026, are $1,637,500. The high R² of 0.96 suggests this prediction is quite reliable within the observed data range.

Source: Khan Academy · Last updated: April 2026

Frequently Asked Questions

What does R-squared mean in regression?
R-squared measures the proportion of variance in the dependent variable explained by the independent variable(s). An R-squared of 0.85 means the model explains 85% of the variation. Higher values indicate a better fit, but R-squared alone does not prove causation.
What is the difference between correlation and regression?
Correlation measures the strength and direction of the linear relationship between two variables (-1 to +1). Regression goes further by providing an equation (y = mx + b) that predicts one variable from the other and quantifies the rate of change.
When should I use linear regression?
Use linear regression when you expect a linear relationship between variables, your data has roughly constant variance, observations are independent, and residuals are approximately normally distributed. Always plot your data first to check for linearity.

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