数据指标波动假设检验统计

eda/recommended_indexs/hypothesis_test.py

@@ -211,6 +211,7 @@ def chi_cal(data):
     t22_theory = temp2[1] * rate2
     #计算卡方值
     X=((t11_count-t11_theory)**2)/t11_theory+((t12_count-t12_theory)**2)/t12_theory+((t21_count-t21_theory)**2)/t21_theory+((t22_count-t22_theory)**2)/t22_theory
+    print("卡方值为：{}".format(X))
     #计算自由度
     v=(len(data)-1)*(data.columns.size-1)
     #查表发现阈值为3.84
@@ -219,7 +220,7 @@ def chi_cal(data):
     else:
         print("数据波动较小，95%可能性属于正常波动范围")
 
-
+#精准点击曝光数据（首页精选日记本列表on_click_diary_card）
 chi_ctr_precise_recently=chi_DATA_recently("clk_count_oldUser_all_a","clk_count_oldUser_all_b","imp_count_oldUser_all_precise","on_click_diary_card",five_days,yesterday)
 temp1=[float(str(Decimal(chi_ctr_precise_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctr_precise_recently))]
 # print(temp1)