Sql 获取BigQuery ML线性回归中的标准误差

我试图在BigQueryML中获得线性回归中Beta的标准误差，如果我错过了一些基本的东西，很抱歉，但是我找不到这个问题的答案

#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)])

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 )

---

但对于许多协变量来说，这将是一个沉重的负担。有没有一种直接的方法可以得到这个置信区间的基本统计数据

AFAIK没有提供标准错误的BigQuery ML函数；尽管如此，and函数是否可以通过使用STDEV和均方误差来帮助您计算标准误差：标准误差=剩余标准误差/输入平方和。AFAIK没有提供标准误差的BigQuery ML函数；尽管如此，and函数是否可以通过使用STDEV和均方误差来帮助您计算标准误差：标准误差=剩余标准误差/输入平方和。

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 )