Helmert's method of least squares

Helmert's method of least squares is a technique used in data analysis to find the best possible estimate of unknown parameters by minimizing the sum of squared differences between observed and predicted values. It does this iteratively: starting with initial guesses, it refines these estimates by accounting for measurement errors and constraints, gradually improving accuracy. The method emphasizes stability and consistency, making it useful in fields like geodesy and surveying, where precise large-scale measurements are common. Essentially, Helmert's approach ensures the most reliable solution by balancing errors across all data points and constraints.