Jupyter Notebook Modify your code to calculate the sample variance: (ti )?. Use the np.sum function as in the previous exercise. Calculate the ratio o sa to the true value o2 = 4. You results should show that for N = 10 the sample variance s is biased from the true value of o2. It turns out that N-1 (s) -02 N which is an optional exercise in the lecture notes. A plausible explanation for this effect comes from noting that the quantity: (1; - u) is unknown whereas our definition of 7 implies that i - z) = 0 Which is a fixed constraint on the N values used to compute the sum in s. This constraint reduces the number of independent measurements to N 1, biasing the result by the factor (N 1)/N. Understanding this bias allows us to caculate the unbiased sample variance as: N-:- 3) 1 N - 1 The use of N 1 instead of N is called Bessel's correction. Jupyter Notebook Modify your code to calculate the sample variance: (ti )?. Use the np.sum function as in the previous exercise. Calculate the ratio o sa to the true value o2 = 4. You results should show that for N = 10 the sample variance s is biased from the true value of o2. It turns out that N-1 (s) -02 N which is an optional exercise in the lecture notes. A plausible explanation for this effect comes from noting that the quantity: (1; - u) is unknown whereas our definition of 7 implies that i - z) = 0 Which is a fixed constraint on the N values used to compute the sum in s. This constraint reduces the number of independent measurements to N 1, biasing the result by the factor (N 1)/N. Understanding this bias allows us to caculate the unbiased sample variance as: N-:- 3) 1 N - 1 The use of N 1 instead of N is called Bessel's correction