获取BigQuery ML线性回归中的标准错误

Question

我正在尝试通过bigquery ML中的线性回归获取beta的标准错误，很抱歉，如果我错过了一些基本知识，但是我找不到该问题的答案

#standard sql
CREATE OR REPLACE MODEL `DATASET.test_lm`   
OPTIONS(model_type='LINEAR_REG', input_label_cols= ["y"]) AS
select * from unnest(ARRAY<STRUCT<y INT64, x float64>> [(1,2.028373), 
(2,2.347660),(3,3.429958),(4,5.250539),(5,5.976455)])

您可以使用

获得没有差异的权重

select * from ml.weights(model `DATASET.test_ml`)  

此外，您可以像这样直接计算标准误差

with dat as (
select * from unnest(ARRAY<STRUCT<y INT64, x float64>> [(1,2.028373), (2,2.347660),(3,3.429958),(4,5.250539),(5,5.976455)])),

#get the residual standard error, using simple df-2  
rse_dat as (
select sqrt(sum(e2)/((select count(1) from dat)-2)) as rse from (
select pow(y - predicted_y, 2) as e2 from ml.predict(model  `DATASET.test_lm`,
(select * from dat)))),

#get the variance of x
xvar_dat as (
select  sum(pow(x - (select avg(x) as xbar from dat),2)) as xvar from dat)

#calulate standard error 
select sqrt((select pow(rse,2) from rse_dat)/(select xvar from xvar_dat) as beta_x_se )

但这对于许多协变量来说是沉重的负担。是否有直接方法可以使此基本统计数据置信区间？