Linear Regression Calculator

Uncover relationships in your data with our linear regression tool. Enter your data to calculate the best-fit linear equation and understand the strength of the relationship between variables.

Input Data

Independent Variable(s) (X):

Multiple independent variables can be entered as columns.

Dependent Variable (Y):

Enter a single column of values for the dependent variable.

Regression Results

Coefficients (β):

R-squared (R²):

P-values:

Understanding Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It aims to find the best-fitting linear equation to describe how the dependent variable changes as the independent variable(s) change.

Coefficients: These are the estimated values that represent the change in the dependent variable for a unit change in the independent variable. In simple linear regression (one independent variable), there's an intercept and a slope.
R-squared: It measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It ranges from 0 to 1, where a higher value generally indicates a better fit.
P-values: These values assess the statistical significance of each coefficient. A low p-value (typically < 0.05) suggests that the coefficient is statistically significant, meaning the independent variable is likely to have a significant effect on the dependent variable.

This tool helps you perform linear regression analysis, providing these key metrics and a visualization to understand your data better. For further learning, you can explore resources on statistical analysis and regression models.