forked from Yesen/python_math_stat
68 lines
1.5 KiB
Python
68 lines
1.5 KiB
Python
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# ДИСПЕРСИЯ
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# list_data = [int(value) for value in input().split()]
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# n = len(list_data)
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# Sum = 0
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# for value in list_data:
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# Sum += value
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# SUm=Sum/n
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# Sum = 0
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# for value in list_data:
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# a = (value - SUm)**2
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# Sum += a
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# D = (Sum / (n - 1 ))
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# print (D)
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# Среднеквадратичное отклонение sd
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# list_data = [int(value) for value in input().split()]
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# n = len(list_data)
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# Sum = 0
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# for value in list_data:
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# Sum += value
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# SUm=Sum/n
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# Sum = 0
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# for value in list_data:
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# a = (value - SUm)**2
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# Sum += a
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# D = (Sum / (n - 1 ))
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# sd = D ** 0.5
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# print (sd)
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# находим среднее значение
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def find_average(x):
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return sum(x) / len(x)
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# находим размах
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def find_range(x):
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x_copy = sorted(x)
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return abs(x_copy[-1] - x_copy[0])
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# находим медиану
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def find_median(x):
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if len(x) % 2 == 0:
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return (x[len(x) // 2] + x[len(x) // 2 -1]) / 2
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else:
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return x[len(x) // 2]
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# находим дисперсию в генеральной совокупности
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def find_general_variance(x, x_average):
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variance = 0
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for i in x:
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variance += (i - x_average) ** 2
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return variance / len(x)
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# находим дисперсию в выборке
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def find_subgeneral_variance(x, x_average):
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variance = 0
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for i in x:
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variance += (i - x_average) ** 2
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return variance / (len(x) - 1)
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# возвращаем квадрат из числа
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def get_sqrt(x):
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return x ** 0.5
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