Residual Values (Residuals) in Regression Analysis - Statistics How To

When you perform simple linear regression (or any other type of regression analysis), you get a line of best fit. The data points usually don’t fall exactly on this regression equation line; they are scattered around. A residual is the vertical distance between a data point and the regression line. Each data point has one residual. They are: As residuals are the difference between any data point and the regression line, they are sometimes called “errors.” Error in this context doesn’t mean that there’s something wrong with the analysis; it just means that there is some unexplained difference. In other words, the residual is the error that isn’t explained by the regression line. The residual(e) can also be expressed with an equation. The e is the difference between the predicted value (ŷ) and the observed value. The scatter plot is a set of data points that are observed, while the regression line is the prediction. The sum of the residuals always equals zero (assuming that your line is

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• Thục Khuê Vũ