Ordinary least squares (OLS) is a method of regression in which the parameters are estimated by:

• A. Maximum likelihood estimation
• B. Bayesian inference
• C. The method of least squares
• D. Bootstrapping methods

Answer: (C)

Ordinary least squares estimates parameters by minimizing the sum of squared residuals—the differences between the observed values and the values predicted by the linear model.

In a linear regression y = Xβ + ε, we choose β to minimize S(β) = (y − Xβ)′(y − Xβ). Differentiating and setting to zero gives the normal equations X′Xβ̂ = X′y, whose solution is β̂ = (X′X)⁻¹X′y when X′X is invertible. This is the defining procedure of OLS.

That’s why the correct choice is the method of least squares. Note that maximum likelihood estimation is a broader framework; with normally distributed errors, the MLE for β coincides with the OLS estimates, but the OLS answer specifically refers to least squares. Bootstrapping and Bayesian inference describe different approaches (resampling or priors/posteriors) and are not the method OLS uses to transfer data into parameter estimates.