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Ordinary Least Squares

Ordinary Least Squares (OLS) is a linear regression method. It minimizes root mean square errors.

Contents

Ordinary Least Squares
1. Univariate
2. Linear Model

Univariate

The regression line passes through two points:

and

These points, with the generic equation for a line, can prove that the slope of the regression line is equal to:

The generic formula for the regression line is:

Linear Model

The linear model can be expressed as:

If these assumptions can be made:

Linearity
Exogeneity

Random sampling
No perfect multicolinearity
Heteroskedasticity

Then OLS is the best linear unbiased estimator (BLUE) for these coefficients.

Using the computation above, the coefficients are estimated to produce:

The variance for each coefficient is estimated as:

Where R² is calculated as:

Note also that the standard deviation of the population's parameter is unknown, so it's estimated like:

CategoryRicottone

Statistics/OrdinaryLeastSquares (last edited 2025-01-10 14:33:38 by DominicRicottone)

-  ⇤ ← Revision 1 as of 2023-10-28 05:18:15 → 
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  Editor: DominicRicottone
  Comment:
+   ← Revision 7 as of 2023-10-28 16:21:41 → ⇥
  Size: 1381
  Editor: DominicRicottone
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
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-= Linear Regression =
+= Ordinary Least Squares =
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-A linear regression expresses the linear relation of a treatment variable to an outcome variable.
+'''Ordinary Least Squares''' ('''OLS''') is a linear regression method. It minimizes root mean square errors.
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-== Regression Line ==

A regression line can be especially useful on a scatter plot.
+== Univariate ==
-Line 23:
+Line 21:
+These points, with the generic equation for a line, can [[Econometrics/OrdinaryLeastSquares/UnivariateProof|prove]] that the slope of the regression line is equal to:

{{attachment:b12.svg}}

The generic formula for the regression line is:

{{attachment:b13.svg}}
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+Line 33:
-== Regression Computation ==
+== Linear Model ==
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+Line 35:
-Take the generic equation form of a line:
+The linear model can be expressed as:
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+Line 37:
-{{attachment:b01.svg}}
+{{attachment:model1.svg}}
-Line 33:
+Line 39:
-Insert the first point into this form.
+If these assumptions can be made:
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+Line 41:
-{{attachment:b02.svg}}
+. Linearity
 2. Exogeneity
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+Line 44:
-This can be trivially rewritten to solve for ''a'' in terms of ''b'':
+{{attachment:model2.svg}}
-Line 39:
+Line 46:
-{{attachment:b03.svg}}
+.#3 Random sampling
 4. No perfect multicolinearity
 5. Heteroskedasticity
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+Line 50:
-Insert the second point into the original form.
+Then OLS is the best linear unbiased estimator ('''BLUE''') for these coefficients.
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+Line 52:
-{{attachment:b04.svg}}
+Using the computation above, the coefficients are estimated to produce:
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+Line 54:
-Now additionally insert the solution for ''a'' in terms of ''b''.
+{{attachment:model3.svg}}
-Line 47:
+Line 56:
-{{attachment:b05.svg}}
+The variance for each coefficient is estimated as:
-Line 49:
+Line 58:
-Expand all terms to produce:
+{{attachment:model4.svg}}
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+Line 60:
-{{attachment:b06.svg}}
+Where R^2^ is calculated as:
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+Line 62:
-This can now be eliminated into:
+{{attachment:model5.svg}}
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+Line 64:
-{{attachment:b07.svg}}
+Note also that the standard deviation of the population's parameter is unknown, so it's estimated like:
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+Line 66:
-Giving a solution for ''b'':

{{attachment:b08.svg}}

This solution is trivially rewritten as:

{{attachment:b09.svg}}

Expand the formula for correlation as:

{{attachment:b10.svg}}

This can now be eliminated into:

{{attachment:b11.svg}}

Finally, ''b'' can be eloquently written as:

{{attachment:b12.svg}}

Giving a generic formula for the regression line:

{{attachment:b13.svg}}
+{{attachment:model6.svg}}

Diff for "Statistics/OrdinaryLeastSquares"

Ordinary Least Squares

Univariate

Linear Model