An estimating model that determines parameter weights based on the set that minimizes the sum of the squared deviation of predicted values from a line or set of observed values.
any line- or curve-fitting model that minimizes the squared distance of data points to the line.
a method of fitting a curve to data points so as to minimize the sum of the squares of the distances of the points from the curve
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.