1, correlation coefficient:,
When r > 0, the two variables are positively correlated; When r < 0, the two variables are negatively correlated; |r|≤ 1, and the closer |r| is to 1, the greater the correlation; The closer |r| is to 0, the smaller the correlation is.
2. Remaining:
Correlation index R2 is used to describe the effect of regression, and its calculation formula is:
In a linear model with an explanatory variable, R2 is exactly equal to the square of the correlation coefficient R. Obviously, the larger the value of R2, the smaller the sum of squares of residuals, that is, the better the fitting effect of the model.