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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 testretest 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.
•
Since the mean of the residuals is 0, we want the points on the residual
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This note was uploaded on 07/26/2011 for the course STA 528 taught by Professor Calder during the Winter '09 term at Ohio State.
 Winter '09
 Calder
 Statistics

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