When there is more than one explanatory variable, R-squared can be misleading.
R-squared will almost always increase as more variables are added, even if those variables are irrelevant.
This happens because additional variables create more data points around the regression line, which can falsely suggest a better fit.
Adjusted R-squared addresses this issue by accounting for the number of explanatory variables.
Therefore, it is better to focus on the Adjusted R-squared value when dealing with multiple explanatory variables.
In this case, the Adjusted R-squared value is 0.814.
R-squared ranges from 0 to 1 (0% to 100%).
A high R-squared value indicates that many data points are close to the regression line, while a low R-squared value suggests a poor fit.
Conclusion: The model fits the data well!
Congratulations on completing the final module of the data science library!
