In an uncorrelated data set, for a given value of x, the best-predicted value of y is the mean. If the variables have a linear correlation, a y-value can be predicted by substituting the x-value in the regression equation. The vertical distance between the predicted y-value and the sample mean, y-bar, is known as the explained deviation. The relationship between the two variables can explain this deviation. The vertical distance between the data point and the predicted y-value is known as the unexplained deviation or the residual. The relationship between the variables cannot explain this deviation; it may be due to chance alone or the involvement of other variables. The sum of the unexplained and explained deviations gives the total deviation. Squaring the deviations and summing them for all data points yields the amount of unexplained, explained, and total variation. The ratio of the explained variation to the total variation is the r-square value, also known as the coefficient of determination. It indicates the proportion of the variation in the y-value that the regression line can explain.