we are doing Least squares curve fitting for numerical calculus. Please explain in detail what each line is doing.

order to return the values of the parameters that best minimize the sum of the squared residuals. The following blocks of Python code implement the idea in a very streamlined way. Go through the code and comment each line to describe exactly what it does import numpy as np import matplotlib.pyplot as pit from scipy optimize import minimize xdata = np.array([0, 1, 2, 31) ydata = np.array([1.07, 3.9, 14.8. 26.0)) def SSRes(parameters): # In the next Line of code we want to build our quadratic approximation y = x2 + x + We are sending in a list of parameters 50 #o paraneterso. b. parameters [1], and parameters (2) yapprox - parameters[@]*xdata? parameters [1]xdata. parameters [2] residuals . np.abs(ydata-yapprox) return np. sun(residuals 2) BestParameters minisize(SSRes, t2,2,0.751) print("The best values of #, b, and care: In", BestParameters.) If you want to print the diagnosite then use the line below: #print("The minimization diagnostics are: In", BestParameters) # The best values of a, b, and care! ** (2.29249989 1.93150033 8.72149989) plt.plot(xdata,ydata, 'bo'markersize-5) x. np. linspace(0,4,100) y BestParameters.x[@]****2 + Best Parameters. [1] BestParameters. [2] plt.plot(...) plt.grid) pit.xlabel('x') plt.yiabely plt.title('Best Fit Quadratic) pit.show()