In the context of parametrized predictors, which estimation technique is commonly used to determine the parameters of a regression model?

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Seekh · Accepted Answer

In regression with parametrized predictors, the most common way to find the model parameters is ordinary least squares (OLS). OLS works by choosing the values of the coefficients that make the sum of squared differences between the observed outcomes and the predictions as small as possible. This is done by solving a simple set of linear equations derived from setting the derivative of that sum to zero. For example, if you have a model $y = \beta_0 + \beta_1 x$, OLS will give you the $\beta_0$ and $\beta_1$ that best fit the data points in a least‑squares sense.

In the context of parametrized predictors, which estimation technique is commonly used to determine the parameters of a regression model?

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