Correlation Coefficient over Correlation Determination in linear regression

Question

i am new to machine learning and i am using housing price dataset from kaggle.com to solve regression problem. i want to know the difference between Correlation Coefficient and Correlation Determination and why people use one over the other. for instance, i can see the relation between YearBuild and SalePrice like this

now, what is the use of Coefficient Determination, why is it used

if R= Coeffiecient Corellation then Coefficient Determination = R x R

1. is the percentage view of the Corellation Coeffiecient?
2. is it the relation of an individual feature with the rest of the feature?

Answer 1

The coefficient R squared tells you how much of the variance the regression model explains. If it is equal to 0.01 for example, it means that you have explained one percent of the variance. This is useful to know for obvious reasons. Unlike the correlation coefficient, R squared is always positive so just tells you that there is (or is not) a linear relationship, but not what its form is.