Rsquare

R-squared, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that is explained by an independent variable or variables in a regression model. It is a value between 0 and 1, where 0 indicates that the model explains none of the variability of the response data around its mean, and 1 indicates that the model explains all the variability.

In simpler terms, R-squared tells you how well your regression line fits the data. A higher R-squared value means that the model's predictions are closer to the actual observed values. For example, if an R-squared value is 0.75, it means that 75% of the variation in the dependent variable can be explained by the independent variable(s) in the model.

It is important to note that a high R-squared does not necessarily mean that the regression model is good or that the independent variables cause the dependent variable. Correlation does not imply causation. Furthermore, R-squared can be misleading if additional independent variables are added to the model, as it will always increase or stay the same, even if the new variables are not significant. This is why adjusted R-squared is often used, which penalizes the addition of irrelevant variables. R-squared is a valuable tool for assessing the goodness-of-fit of a regression model, but it should be interpreted in conjunction with other statistical measures and domain knowledge.