(testData) <50: p_value= stats.shapiro(testData)[1] if p_value<0.05: print "use shapiro:" print "data are not normal distributed" return False else: print "use shapiro:" print "data are normal distributed" return True if 300>=len(testData) >=50: p_value= lillifors(testData)[1...
Tests for normality distribution of given data set :param arr: ndarray object to generate statistics on :return: """ print("Skew of data set %14.3f" % scs.skew(arr)) print("Skew test p-value %14.3f" % scs.skewtest(arr)[1]) print("Kurt of data set %14.3f" % scs.kurtosis(arr)...
defnormaltest(a, axis=0, nan_policy='propagate'):"""Test whether a sample differs from a normal distribution. This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D'Agostino and Pearson's [1]_, [2]_ test that combines skew and kurtosis...
Test whether a sample differs from a normal distribution. This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D'Agostino and Pearson's [1]_, [2]_ test that combines skew and kurtosis to produce an omnibus test of normality. Parameters --...
二项式分布(Binomial Distribution) 均匀分布(Uniform Distribution) 泊松分布(Poisson Distribution) 正态分布(Normal Distribution) 长尾分布(Long-Tailed Distribution) 学生t 检验分布(Student’s t-test Distribution) 对数正态分布(Lognormal Distribution)
正态分布(Normal Distribution) 长尾分布(Long-Tailed Distribution) 学生t 检验分布(Student’s t-test Distribution) 对数正态分布(Lognormal Distribution) 指数分布(Exponential Distribution) 威布尔分布(Weibull Distribution) 伽马分布(Gamma Distribution)
# 创建一个具有偏斜分布的样本数据集np.random.seed(0)data = np.random.exponential(scale=2, size=(1000, 1)) # 指数分布 # 初始化QuantileTransformerquantile_transformer = QuantileTransformer(n_quantiles=100, output_distribution=...
均匀分布(Uniform Distribution) 泊松分布(Poisson Distribution) 正态分布(Normal Distribution) 长尾分布(Long-Tailed Distribution) 学生t 检验分布(Student’s t-test Distribution) 对数正态分布(Lognormal Distribution) 指数分布(Exponential Distribution)
plt.figure(figsize=(10,6))plt.hist(data,bins=30,density=True,alpha=0.6,color='g')# 绘制正态分布曲线xmin,xmax=plt.xlim()x=np.linspace(xmin,xmax,100)p=stats.norm.pdf(x,np.mean(data),np.std(data))plt.plot(x,p,'k',linewidth=2)plt.title('Histogram of Normal Distribution')plt.xl...
normality_test(log_returns.flatten()) 如此之后,大概能看到这样的结果: 这个正态分布的假设检验的零假设当然就是分布是正态分布的。结果我们发现,p-value很大,所以我们不能拒绝原假设。当然,这一块逻辑是存在一定缺陷的,也就是说,我们不能拒绝原假设,但是统计意义是,其实我们并不能直接接受这就是正态分布这一...