# 将数据放入Pandas DataFrame中 df = pd.DataFrame({ 'y': y, 'x1': x1, 'x2': x2 }) # 使用statsmodels的RLM类进行稳健回归 model = smf.rlm("y ~ x1 + x2", data=df).fit() # 打印回归结果 print(model.summary()) 在这个例...
sm from statsmodels.api import OLS # 导入数据 boston = load_boston() data = pd.DataFrame(data = boston['data'], columns = boston['feature_names']) target = pd.Series(boston['target']) # 开发模型 sm_lm = OLS(target, sm.add_constant(data)) result = sm_lm.fit() result.summary()...
summary_table = pd.DataFrame() for i in range(4): print('每个用户七八月电量%s与三四月电量%s的比值:'%(col[i],col[i])) ratio_table = date_merge(mon_78[col[i]], mon_34[col[i]], names[i]) summary_table[names[i]] = ratio_table[names[i]] print(ratio_table,'\n') print('...
importnumpy as npimportpandas as pd inputfile='D://CourseAssignment//AI//DataPredict//data.csv'data=pd.read_csv(inputfile)#describe statistical analysisdescription =[data.min(), data.max(), data.mean(), data.std()]#calculate the min, max, mean, stddescription = pd.DataFrame(description,...
Often, you might just pass them to a NumPy or SciPy statistical function. In addition, you can get the unlabeled data from a Series or DataFrame as a np.ndarray object by calling .values or .to_numpy().Getting Started With Python Statistics Libraries The built-in Python statistics library ...
cols, col_colors_dict = data dfName: seaborn Version: 0.11.1 Summary: seaborn: statistical ...
Let’scheck the classes of all the columnsin our new pandas DataFrame: print(data_import.dtypes)# Check column classes of imported data# x1 int32# x2 object# x3 int32# x4 object# dtype: object As you can see, the variables x1 and x3 are integers and the variables x2 and x4 are co...
DataFrame({ "g": np.random.choice(["Yes", "No"], size=50), "x1": np.random.normal(size=50), "x2": np.random.normal(size=50) }) Here we just add the family argument set to "bernoulli" to tell Bambi we are modelling a binary response. By default, it uses a logit link. ...
Statistical Significance Of Coefficients 统计显著性首先:我们要测试这个斜塔的倾斜程度是否与年份有关,我们将null hypothesis(H0):没有关系,也就是年份,这个属性的系数(coefficient )β1=0,相反β1≠0。 1.提出一个假设:H0:β1=0 H1:β1≠0 然后测试null hypothesis,我们需要利用T分布计算一个统计测量值: ...
Results dataframe for delta-delta and mini-meta plots: A results dataframe can now be extracted for both the delta-delta and mini-meta effect size data (similar to the results dataframe for the regular effect sizes). These can be found via the.resultsattribute of the.delta_deltaor.mini_meta...