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Multiple Regression Calculator

Enter Your Data

This multiple regression calculator helps you analyze relationships between variables. Used worldwide in economics, healthcare, social sciences, and business analytics.

Instructions:

  • Enter numeric values only in all fields
  • Include at least 5 data points for reliable results
  • Values can be whole numbers or decimals

Frequently Asked Questions

What is a multiple regression calculator?
A multiple regression calculator is a statistical tool that analyzes the relationship between one dependent variable and two or more independent variables. It helps predict outcomes and understand which factors most influence results.
How accurate are multiple regression calculations?
Accuracy depends on data quality, sample size, and model specification. Most calculators provide R-squared values showing what percentage of the dependent variable’s variation is explained by the independent variables.
Can I use this calculator for medical or financial decisions?
This calculator provides statistical estimates only. For medical, financial, or legal decisions, consult appropriate professionals. Statistical results should inform but not replace expert judgment.

Regression Results

Your multiple regression analysis results will appear here after calculation. The calculator uses standard ordinary least squares (OLS) methodology.

Results are based on statistical estimation. Consider confidence intervals and p-values when interpreting coefficients.

Regression Equation

Y = β₀ + β₁X₁ + β₂X₂ + … + ε

Coefficients & Significance

The table below shows estimated coefficients for each independent variable in your multiple regression model:

Variable Coefficient Standard Error t-Statistic p-Value

Model Statistics

These statistics help evaluate your multiple regression model’s overall performance:

Statistic Value Interpretation

International Standards Applied

This calculator follows these international statistical standards:

Region/Organization Standard Application
American Statistical Association Ethical Guidelines Statistical practice
World Health Organization Health Metrics Health-related variables
International Monetary Fund Economic Reporting Economic variables
European Union Data Protection Privacy standards

Interpretation Guidance

When interpreting your multiple regression results:

  • R-squared: Higher values (closer to 1) indicate better model fit
  • Coefficients: Represent change in Y per unit change in X, holding other variables constant
  • p-values: Values below 0.05 suggest statistically significant relationships
  • Confidence intervals: Provide range of plausible values for coefficients

For fitting curves of any degree to your data, use the Polynomial Regression Calculator on OnlineFreeCalculators.org to find the best-fit polynomial equation quickly.

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