The model for a regression optimization is y = a + b x , and the objective is to determine values for coefficients “a” and “b” from experimental (x, y) data pairs. Independent variable, x, ranges from...

The model for a regression optimization is y = a + b_x, and the objective is to determine values for coefficients “a” and “b” from experimental (x, y) data pairs. Independent variable, x, ranges from 0 to 10, 0 ≤ x ≤ 10. Once the model is determined, we want the maximum uncertainty on y, ε_y, to be <0.01.>_a
and ε_b. (ii) If a numerical optimizer were used for regression, relate the values of ε_a
and ε_b
to the convergence criteria. Remember that ε_a
and ε_b
are positive numbers representing a “+” or “−” deviation.