Multiple Linear Regression Calculator
This calculator is designed for statisticians and data scientists to compute multiple linear regression models, helping to understand the relationship between variables.
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Data Source and Methodology
All calculations are based strictly on the formulas and data provided by standard statistical models. For further details, please refer to authoritative statistical texts.
The Formula Explained
The formula for multiple linear regression is: Y = β0 + β1X1 + β2X2 + ... + βnXn + ε
Glossary of Terms
- Independent Variable: Variables that are manipulated to determine their effect on the dependent variable.
- Dependent Variable: The outcome variable that the model aims to predict.
- Coefficients: Numeric values that multiply the independent variables in the regression equation.
- R-Squared: A statistical measure that represents the proportion of variance for the dependent variable explained by the independent variables.
Frequently Asked Questions (FAQ)
What is Multiple Linear Regression?
Multiple Linear Regression is a statistical technique used to model the relationship between one dependent variable and two or more independent variables.
How to interpret the coefficients?
The coefficients represent the change in the dependent variable for a one-unit change in the independent variable, holding other variables constant.
What is R-Squared?
R-Squared indicates how well the independent variables explain the variability of the dependent variable. A higher value indicates a better fit.
What are the assumptions of Multiple Linear Regression?
Common assumptions include linearity, independence, homoscedasticity, and normality of residuals.