👉 In statistics, a "pipe fitting" is an algorithm or procedure for estimating parameters of a model by minimizing a functional that incorporates both the error and the variance of the observed data. This approach involves first estimating the model's parameters through least squares regression, which minimizes the sum of squared errors (SSE) between the predicted values and the observed data points. Then, using the minimized SSE as the estimate of the model's parameter(s), the algorithm performs a further estimation step to reduce the variance
