One of the fancy mathematical techniques for fudging survey data. It has a specific claim to fame of being statistically likely to to produce a result that could represent some sort of reality with a high probability.

The most common method of training (estimating) the weights (parameters) of a model by choosing the weights that minimize the sum of the squared deviation of the predicted values of the model from the observed values of the data.

Least squares or ordinary least squares (OLS) is a mathematical optimization technique which, when given a series of measured data, attempts to find a function which closely approximates the data (a "best fit"). It attempts to minimize the sum of the squares of the ordinate differences (called residuals) between points generated by the function and corresponding points in the data. Specifically, it is called least mean squares (LMS) when the number of measured data is 1 and the gradient descent method is used to minimize the squared residual.