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# curve_fitting | ||
# 1. Least-Squares | ||
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# Set 1 | ||
Given Function: | ||
y = 0.3745 + 0.98644x + 0.84564x^-1 | ||
Regression Function: | ||
y = 0.3745 + 0.9864x + 0.8456x^-1 | ||
R2: | ||
0.9996 | ||
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# Set 2 | ||
Given Function: | ||
y = -11.4887 + 7.143817x - 1.04121x^2 + 0.046676x^3 | ||
Regression Function: | ||
y = -11.4887 + 7.1438x - 1.0412x^2 + 0.0467x^3 | ||
R2: | ||
0.8290 | ||
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# Set 3 | ||
Given Function: | ||
y = 4.0046e^(-1.5x) + 2.9123e^(-0.3x) + 1.5647e^(-0.05) | ||
Regression Function: | ||
y = 4.0046e^(-1.5x) + 2.9123e^(-0.3x) + 1.5647e^(-0.05) | ||
R2: | ||
0.9971 | ||
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# 2. Cost Logistic | ||
cost = 10.1590 | ||
gradient = 101.0435 | ||
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