Unformatted text preview: ationship between x and y.
linear The book uses the formula:
r² = (SSM – SSE) / SSM
r² Couple of Examples:
Couple The variation of each observation (y) from ŷ is small.
ŷ “explains” the variation in y very well
High r, high r² The variation of each observation (y) from ŷ is
not really small.
ŷ doesn’t “explain” the variation in y as well.
Poor r, poor r² Example
Example Suppose from our strong example that
Suppose
r = .9 then r² = .81
This means that 81% of the variation in the y
This
variable is accounted for by the linear
relationship between x and y
relationship Suppose the other model:
r = .4 then r² = .16
This means that only 16% of the variation in the
This
y variable is accounted for by the linear
relationship
relationship Some points
Some Always use in context Must interpret the r² with our sentence. Do
Must
not say:
not The regression equation can predict 81% of
The
the data points
the 81% of data points lie on the LSRL LSRL accounts for 81% of the data points Properties of r
Properties 2 Prope...
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This document was uploaded on 03/30/2014 for the course MATH AP Statist at Richard Montgomery High.
 Winter '13
 Jessell
 Coefficient Of Determination

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