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Using numpy arrays, I wrote a function for you (I used Google Gemini for TSS and RSS):

def coeff_of_det(R,coeffs):
    import numpy as np
    # Extract x and y values
    x_values = np.array([point[0] for point in R])
    y_true = np.array([point[1] for point in R])

    # Predict y values
    y_pred = [model.subs(coeffs)(x_val) for x_val in x_values]

    # R**2
    y_mean = n(sum((y_true)) / len(y_true))
    TSS = sum((y_true - y_mean)**2)
    RSS = sum((y_true - y_pred)**2)
    r_squared = 1 - (RSS / TSS)
    return r_squared


R = [[1, 2], [3.45, 4], [6, 5], [4, 3]]
var('a, b')
model(x) = a * x + b
coeffs = find_fit(R, model)

r_sq=coeff_of_det(R,coeffs)

print(r_sq)