Least Squares Curve Fitting
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Resource Overview
Least squares curve fitting algorithm with coefficient calculation and GUI interface for visualization
Detailed Documentation
Least squares curve fitting is a widely used regression method that optimizes curve parameters by minimizing the sum of squared residuals between observed data and fitted values. The implementation typically involves solving linear equations using matrix operations (e.g., MATLAB's polyfit function or numpy.polyfit in Python). A GUI interface provides interactive controls for data input, parameter adjustment, and real-time visualization of fitting results with error metrics.
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