首先,我们看一下使用statistics模块的一个例子: importstatistics# 示例数据data=[10,12,23,23,16,23,21,16]# 计算样本标准偏差sample_std_dev=statistics.stdev(data)print(f"样本标准偏差:{sample_std_dev}") 1. 2. 3. 4. 5. 6. 7. 8. 9. 在这个代码示例中,我们通过s
/usr/local/bin/pythonimport numpyasnp arr=np.array([1,2,3,4])print("sample standard deviation: np.std()",np.std(arr,ddof=1))liumiaocn:tmpliumiao$ python np-7.py('sample standard deviation: np.std()',1.2909944487358056)liumiaocn:tmpliumiao$ 1. 2. 3. 4. 5. 6. 7. 8. 注意:...
样本均值(sample mean): \bar{x} = \frac{\sum_{i=1}^{n}x_{i}}{n} = \frac{x_{1}+x_{2}+\cdots +x_{n}}{n} \tag{1}样本方差(sample variance): s^2 = \frac {\sum_{i=1}^{n}(x_i - \bar{x})^2} {n-1} \tag{2}样本标准差(sample standard deviation): s = \sq...
# calculate sample standard deviationsstd1, std2 = std(data1, ddof=1), std(data2, ddof=1) 然后计算标准误差: # calculate standard errorsn1, n2 = len(data1), len(data2)se1, se2 = std1/sqrt(n1), std2/sqrt(n2) 另外,我们可以使用SciPy中的sem() 函数直接计算标准误差。 # calculate s...
sum_dev = narray_dev.sum() DEV = float(sum_dev) / float(N) STDEV = numpy.math.sqrt(DEV) print "mean:", mean, "; DEV:", DEV, "; STDEV:", STDEV return mean, DEV, STDEV均值为mean，方差为DEV，标准差是STDEV传入数据是一个list：sum_list_in standard deviati...
print(np.std(my_array, ddof = 1)) # Get standard deviation of all array values # 2.4201534780139164The previous result is slightly higher as in Example 1, since we have calculated the sample standard deviation instead of the population standard deviation....
standard_deviation = math.sqrt(variance) print(str(standard_deviation)) # result = 0.5034812645401129 #当然如果你使用了numpy,那么求标准差将会十分的简单: import numpy as np # Sample Date - SH000300 Earning in 2017-03 datas = [0.16, -0.67, -0.21, 0.54, 0.22, -0.15, -0.63, 0.03, 0.88,...
Standard deviation is a number that describes how spread out the values are.A low standard deviation means that most of the numbers are close to the mean (average) value.A high standard deviation means that the values are spread out over a wider range....
importnumpyasnpdefstd(data):"""Compute standard deviation of sample:param data: array_like data:return: standard deviation of sample"""returnnp.std(data,ddof=1)defmain():d1=[177,193,195,209,226]d2=[192,197,200,202,209]print("the standard deviation of data1:",std(d1))print("the...
若每次抽样取50个人求平均值,抽100次,这100个平均值的分布仍然会是正态分布,而且mean of sample means还是一样。不同的是,这次的标准差(standard deviation)更小(数据更集中,正态分布的尖更尖) 假设总体工资的标准差为σ,抽样检测得到的“平均值的分布的标准差”(即标准误——standard error,见:通俗理解标准差...