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This reduced form can be quickly solved for ''β''.
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Because the correlation coefficient can be expressed in terms of covariance and standard deviations...

{{attachment:correlation.svg}}

...the solution for ''β'' can be further reduced.

Ordinary Least Squares Univariate Proof

The model is constructed like:

model1.svg

This is estimated as:

model2.svg

This line must pass through the mean and the slope of the line must be the marginal change in Y given a unit change in X. In other words, the line must pass through two points:

model3.svg

where:

  • X‾ is the sample mean of X (estimating μX)

  • Y‾ is the sample mean of Y (estimating μY)

  • sX is the sample standard deviation of X (estimating σX)

  • sY is the sample standard deviation of Y (estimating σY)

  • and rXY is the sample correlation coefficient between X and Y (estimating ρXY)

Insert the first point into the estimation. This is quickly solved for α.

alpha1.svg

alpha2.svg

Insert the second point and the solution for α into the estimation.

beta1.svg

beta2.svg

beta3.svg

This reduced form can be quickly solved for β.

beta4.svg

Because the correlation coefficient can be expressed in terms of covariance and standard deviations...

correlation.svg

...the solution for β can be further reduced.

Expand the formula for correlation as:

[ATTACH]

This can now be eliminated into:

[ATTACH]

Finally, b can be eloquently written as:

[ATTACH]

Giving a generic formula for the regression line:

[ATTACH]


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Statistics/OrdinaryLeastSquares/Univariate (last edited 2025-01-10 14:33:54 by DominicRicottone)