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Nonlinear Least Squares

Nonlinear least squares is a method used to find the best-fitting curve or model for a set of data points when the relationship between variables isn't a straight line. Instead of a simple equation, the model can be complex and involves multiple parameters. The technique adjusts these parameters to minimize the differences (the "errors") between the observed data points and the values predicted by the model. This process helps researchers and analysts understand trends and relationships within the data, making it useful in fields like engineering, finance, and science.

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    Nonlinear Least Squares is a mathematical technique used to find the best-fitting curve or model for a set of data points that don't follow a straight line. Unlike linear models, which assume a straight relationship, nonlinear models can represent more complex patterns. The goal is to minimize the difference (or "error") between the observed data points and the values predicted by the model. By adjusting the model parameters, this method helps researchers and analysts understand relationships in data more accurately, leading to better predictions and insights in fields like science, engineering, and economics.