Let's say that you fit a simple regression line y = mx + b to a set of (x,y) data points. In a typical research situation the regression line will not touch all of the points; it might not touch any of them. The vertical difference between the y-co-ordinate of one of the data points and the y value of the regression line for the x-co-ordinate of that data point is called a residual.
There will be one residual for each data point.
To see some labelled diagrams of residuals search images.Google.com for residuals.
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Residual point
The point lies one unit above the regression line.
Ah, the stochastic error term and the residual are like happy little clouds in our painting. The stochastic error term represents the random variability in our data that we can't explain, while the residual is the difference between the observed value and the predicted value by our model. Both are important in understanding and improving our models, just like adding details to our beautiful landscape.
The mathematical term for "mean" is "mean".The popular, or colloquial term for "mean" is "average".
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