Polynomial Quadratic Curve Fitting

This polynomial quadratic curve fitting program is specifically designed for experimental design applications, enabling prediction of experimental outcomes through curve fitting. The core algorithm utilizes quadratic polynomial regression (y = ax² + bx + c) to establish optimal relationships between independent and dependent variables. Additionally, the program integrates a plotting function that visualizes fitted curves against original data points using matplotlib/seaborn libraries for clear graphical representation. For advanced analysis, the system incorporates ANOVA (Analysis of Variance) functionality to statistically validate model significance, determine reliability of experimental results, and assess precision through R-squared metrics and residual analysis. Whether applied in scientific research or engineering design, this program provides robust technical support with features including coefficient calculation, confidence interval estimation, and goodness-of-fit diagnostics.