statistics - SAS stdi output clarification -


i'm attempting convert old sas macros python, , bit unclear of terminology used in sas. in macro, proc statement is

proc reg data=model_file; model &y = &x; output out=&outfile r=resid stdi=resid_error;

i understand r means individual residual per data point, unclear stdi meant. according sas manual, stdi means "standard error of individual predicted value", there 1 stdi each row in dataset. searched around bit , found lecture slide university of wisconsin believe explains how calculate stdi: enter image description here

however, 2 (edit: one) questions remain:

  1. is method calculating standard error of individual prediction in lecture slide indeed correct? i've never seen method before still have doubts. i've looked sas manual, definition stdi bit confusing:enter image description here. specifically, h(i) defined enter image description here don't know bar after [x'x] supposed mean.

  2. the way standard error of individual predictions calculated here utilizes x. however, happens if have run regression multiple x columns? stdi assume single x column?

answer: answer no. can have multiple x columns , still stdi value.

i'm not statistician , question have included lot more detail, quick google search suggests you're looking @ proc reg. main documentation proc reg here:

https://support.sas.com/documentation/cdl/en/statug/63347/html/default/viewer.htm#statug_reg_sect015.htm

and there dedicated page "model fit , diagnostic statistics" including relevant formulae here:

https://support.sas.com/documentation/cdl/en/statug/63347/html/default/viewer.htm#statug_reg_sect039.htm

maybe answer question. although these things don't interest me directly, believe sas's documentation pretty @ describing exact computations being done each procedure.


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