Linked from

The 10 pages that link to Ordinary least squares, each with the reason it gives.

• Linear regressionRelated: It is the standard fitting rule for linear regression.

• CovarianceRelated: In simple regression, the fitted slope depends on the covariance of predictor and outcome.

• Econometric modelBroader topic: It provides a common way to fit linear econometric models.

• MulticollinearityRelated: Its coefficient calculations become unstable when predictor columns are nearly linearly dependent.

• Ridge regressionCompared with: It is the unregularized objective that ridge regression extends.

• Sum of squaresRelated: Its objective function is the residual sum of squares.

• Gauss–Markov theoremBroader topic: The theorem identifies this estimator as best among linear unbiased alternatives.

• Y-interceptRelated: It can estimate the intercept alongside the slope in a linear model.

• Fish length–weight relationshipRelated: It is commonly applied to log-transformed length and weight measurements.

• Frisch–Waugh–Lovell theoremNarrower topic: The theorem concerns coefficients from this estimation method.