Coefficient of determination and Squared error

r^2 = coefficient of determination - Always between zero and one: 0 >= r^2 <= 1 - r^2 is literally just r squared, it’s that easy - \(r^2 = 1 - \frac{SE_{regression}}{SE_\bar{y}}\) - Reads: r squared = 1 minus the squared error of the regression line divided by the squared error of the average of all y values - If squared error is small, it means that the residuals of all points along the regression line are small and they fit the line pretty well - If squared error is big, it means that the residuals of the points along the regression line are big and they don’t fit the line very well - So, r^2 close to 1 is a good fit and r^2 close to zero is a bad fit