OLS estimation involves minimizing the sum of squared differences between observed and predicted values from "summary" of Introduction to Econometrics by Christopher Dougherty
The Ordinary Least Squares (OLS) estimation method aims to find the best-fitting line through a set of data points by minimizing the sum of squared differences between the observed values and the values predicted by the estimated line. This process involves finding the coefficients of the regression model that minimize the sum of the squared residuals, or errors, in the model. The residuals are the differences between the observed values and the values predicted by the regression model. By squaring these residuals and summing them up, OLS ensures that both positive and negative errors are taken into account and that larger errors are penalized ...
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