What is the relationship between R-squared and p-value in linear regression?

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Accepted Answer

R-squared measures the goodness of fit of a regression model, while p-value indicates the significance of the relationship between the independent variable and the dependent variable.

R-squared is a measure of how well the independent variable(s) explain the variability of the dependent variable in a regression model.
A high R-squared value close to 1 indicates a good fit, meaning the model explains a large portion of the variance in the dependent variable.
The p-value in linear regression indicates the significance of the relationship between the independent variable and the dependent variable.
A low p-value (typically less than 0.05) suggests that the independent variable is significantly related to the dependent variable.
In general, a high R-squared value with a low p-value is desirable in linear regression analysis.

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