Ordinary Least Squares

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

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:

1. Linearity

2. Exogeneity

3. Random sampling
4. No perfect multicolinearity

5. Homoskedasticity

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

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

The variances for each coefficient is estimated as:

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

If the homoskedasticity assumption does not hold, then the estimators for each coefficient are actually:

And the variances for each coefficient are estimated as:

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