Since the residual is the difference between the actual point and the trendline, we can say that the formula for calculating each residual is: That is the predicted value of y (y-predicted) is found by the height (y-coordinate) of this line at each value of □. The trendline is a prediction of the y-value at each position. ![]() The fifth point (orange) is above the trendline by a distance of 0.9 and so, its residual is 0.9.The fourth point (green) is below the trendline by a distance of 0.95 and so, its residual is -0.95.The third point (blue) is above the trendline by a distance of 0.7 and so, its residual is 0.7.The second point (pink) is below the trendline by a distance of 2.15 and so, its residual is -2.15.The first point (purple) is above the trendline by a distance of 1.5 and so, its residual is 1.5.The colour of each data point on the scatter plot shows the corresponding residual on the residual plot. ![]() These negative residuals are shown below the axis with a blue arrow. Any points plotted below the regression line on the scatter plot are below the x-axis of the residual plot.These positive residuals are shown above the axis with a red arrow. Any points plotted above the regression line on the scatter plot are above the x-axis of the residual plot.This line on the scatter plot can correspond to the x-axis of the residual plot (also shown in green). The green line on the scatter plot is the linear regression line of best fit. In the example below, we see a scatter plot showing 5 data points and its corresponding residual plot. The larger the residual, the further the point is from the trendline. A positive residual means that the observed value is above the trendline and a negative residual means it is below the trendline. A residual is the difference between the observed value and the value predicted by the model at a given data point.
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