# Diff. in P-value & F-Stat. Multiple linear regression

Even if we have individual p-values for each predictor.

Why do we need overall F-statistic?

I read this solution but I am not sure if I get it right. Can someone please explain?

Source: "An Introduction to Statistical Learning: with Applications in R" by James, Witten, Hastie and Tibshirani

The overall F-stat says if your model is significantly better than the naive model that only has an intercept at the average of all response data pooled together. In other words, it measures if $$R^2$$ is significantly better than 0.
In your case, you can eyeball the $$R^2$$ and see that 0.897 is going to be better than 0. The F-stat and p-value will be more useful when the $$R^2$$ has a subtle difference from 0.