t_stat, p_val = stats.ttest_ind(group1, group2)print(f"t-statistic: {t_stat}, p-value: {p_val}") Python深度学习-意力机制、Transformer模型、生成式模型、目标检测算法、图神经网络、强化学习与可视化方法 Python数据分析实用技巧重新命名列 i...
sed=sqrt(se1**2.0+se2**2.0)# calculate the t statistic t_stat=(mean1-mean2)/sed # degreesoffreedom df=len(data1)+len(data2)-2# calculate the critical value cv=t.ppf(1.0-alpha,df)# calculate the p-value p=(1.0-t.cdf(abs(t_stat),df))*2.0#returneverythingreturnt_stat,df,cv,...
t_value=stats.t(df).isf(a/2) print"t value:",t_value lsd=t_value*math.sqrt(mse*(1.0/len(sample1)+1.0/len(sample2))) print "LSD:",lsd if distance<lsd: print"no significant difference between:",sample1,sample2 else: print"there is significant difference between:",sample1,sample2 ...
#检验平稳性from statsmodels.tsa.stattools import adfuller,kpss #导入库df_stationary_test = pd.read_csv('E:/PythonProject/myproject1/data/a10.csv', parse_dates=['date'])#ADF Testresult = adfuller(df_stationary_test.value.values,autolag='AIC')print(f'ADF Statistic:{result[0]}')print(f'...
df=2*N-2#p-value after comparisonwiththe t p=1-stats.t.cdf(t,df=df)print("t = "+str(t))print("p = "+str(2*p))#Note that we multiply the p value by2because its a twp tail t-test ### You can see that after comparing the t statisticwiththe critical tvalue(computed interna...
from scipy import stats # 两组样本数据 group1 = [1,2,3,4,5,12,3,4,3,4,4,12,3,4,4] group2 = [2,3,4,5,6,13,5,6,5,5,5,15,4,3,2] # 执行独立样本t检验 t_stat, p_val = stats.ttest_ind(group1, group2) print(f"t-statistic: {t_stat}, p-value: {p_val}") ...
= series.index[1:]# check if stationaryresult = adfuller(stationary)print('ADF Statistic: %f' % result[0])print('p-value: %f' % result[1])print('Critical Values:')for key, value in result[4].items():print('\t%s: %.3f' % (key, value...
= df['column'].median()std_dev = df['column'].std()print(f"均值: {mean}, 中位数: {median}, 标准差: {std_dev}")# 假设检验：t检验from scipy import statst_stat, p_value = stats.ttest_1samp(df['column'], popmean=)print(f"t-statistic: {t_stat}, p-value: {p_value}")5...
['收入差分']))#平稳性检测4748#白噪声检验49fromstatsmodels.stats.diagnosticimportacorr_ljungbox50print('差分序列的白噪声检验结果为:', acorr_ljungbox(D_data, lags=1))#返回统计量和p值5152fromstatsmodels.tsa.arima.modelimportARIMA5354#定阶55data['y'] = data['y'].astype('float64')56pmax =...
from statsmodels.tsa.stattools import adfuller, kpssdf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date']) # ADF Testresult = adfuller(df.value.values, autolag='AIC')print(f'A...