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lecture9_6slides - Statistics 528 Lecture 9 Regression...

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Statistics 528 - Lecture 9 1 Statistics 528 - Lecture 9 Prof. Kate Calder 1 Regression Effect/Fallacy Regression Effect: In virtually all test-retest situations, the bottom group on the first test will on average show some improvement on the second test and the top group will on average fall back. This effect is known as the regression effect . Regression Fallacy: The regression fallacy is thinking that the regression effect must be due to something important, not just due to spread about the regression line. Statistics 528 - Lecture 9 Prof. Kate Calder 2 Regression Diagnostics Recall, a residual is the difference between an observed value of the response variable and the value predicted by the regression line. That is, residual i = observed y i - predicted y i = y i - Sum of the residuals is always 0. Mean of the residuals is always 0. i y ˆ Statistics 528 - Lecture 9 Prof. Kate Calder 3 Residual Plot - scatterplot of the residuals against the explanatory variable => help us asses the fit of the regression line.
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