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The majority of the time the actual line will go both above and below the regression line. At any one point, the difference between the regression line and the actual observation is the error.
Regression finds the line that best fits the observations. It does this by finding the line that results in the lowest sum of squared errors. Since the line describes the mean of the effects of the independent variable, by definition, the sum of the actual errors will be zero. In other words, the positive errors will be equal to the negative errors.
In order to evaluate the regression, one method is to check the r2 value...
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