Compute unbiased sample variance using Bessel's correction:
s2=n−11i=1∑n(xi−xˉ)2Then compute sample standard deviation:
s=s2Here, n is the sample count, xi is one observation, and xˉ is the sample mean. Return variance and standard_deviation in a dictionary of Python floats.
Input: x = [1, 2, 3]
Output: {"variance": 1.0, "standard_deviation": 1.0}
Explanation: The squared deviations from the mean sum to 2, and dividing by n minus 1 gives 1.
Input: x = [5, 7]
Output: {"variance": 2.0, "standard_deviation": 1.414214}
Input: x = [4, 4, 4, 4]
Output: {"variance": 0.0, "standard_deviation": 0.0}
Compute centered = x - np.mean(x).
Divide np.sum(centered ** 2) by x.size - 1, then take its square root.
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Accepts: array
Compute unbiased sample variance using Bessel's correction:
s2=n−11i=1∑n(xi−xˉ)2Then compute sample standard deviation:
s=s2Here, n is the sample count, xi is one observation, and xˉ is the sample mean. Return variance and standard_deviation in a dictionary of Python floats.
Input: x = [1, 2, 3]
Output: {"variance": 1.0, "standard_deviation": 1.0}
Explanation: The squared deviations from the mean sum to 2, and dividing by n minus 1 gives 1.
Input: x = [5, 7]
Output: {"variance": 2.0, "standard_deviation": 1.414214}
Input: x = [4, 4, 4, 4]
Output: {"variance": 0.0, "standard_deviation": 0.0}
Compute centered = x - np.mean(x).
Divide np.sum(centered ** 2) by x.size - 1, then take its square root.
Sign in to take notes on this problem
Accepts: array