[
https://issues.apache.org/jira/browse/SPARK-28663?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li resolved SPARK-28663.
-----------------------------
Resolution: Later
> Aggregate Functions for Statistics
> ----------------------------------
>
> Key: SPARK-28663
> URL: https://issues.apache.org/jira/browse/SPARK-28663
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Yuming Wang
> Priority: Major
>
> ||Function||Argument Type||Return Type||Partial Mode||Description||
> |{{corr(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|correlation coefficient|
> |{{covar_pop(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|population covariance|
> |{{covar_samp(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|sample covariance|
> |{{regr_avgx(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|average of the independent variable
> ({{sum(_{{X_}})/_{{N}}_}})|
> |{{regr_avgy(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|average of the dependent variable ({{sum(_{{Y_}})/_{{N}}_}})|
> |{{regr_count(_Y_}}, _{{X}}_)|{{double precision}}|{{bigint}}|Yes|number of
> input rows in which both expressions are nonnull|
> |{{regr_intercept(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|y-intercept of the least-squares-fit linear equation
> determined by the (_{{X}}_, _{{Y}}_) pairs|
> |{{regr_r2(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|square of the correlation coefficient|
> |{{regr_slope(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|slope of the least-squares-fit linear equation determined by
> the (_{{X}}_, _{{Y}}_) pairs|
> |{{regr_sxx(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|{{sum(_{{X_}}^2) - sum(_{{X}}_)^2/_{{N}}_}} (“sum of squares”
> of the independent variable)|
> |{{regr_sxy(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|{{sum(_{{X_}}*_{{Y}}_) - sum(_{{X}}_) *
> sum(_{{Y}}_)/_{{N}}_}} (“sum of products”of independent times dependent
> variable)|
> |{{regr_syy(_Y_}}, _{{X}}_)|{{double precision}}|{{double
> precision}}|Yes|{{sum(_{{Y_}}^2) - sum(_{{Y}}_)^2/_{{N}}_}} (“sum of squares”
> of the dependent variable)|
> [https://www.postgresql.org/docs/11/functions-aggregate.html#FUNCTIONS-AGGREGATE-STATISTICS-TABLE]
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