. ▁The ▁only ▁difference ▁is ▁the ▁interpretation ▁and ▁the ▁assumptions ▁which ▁have ▁to ▁be ▁im posed ▁in ▁order ▁for ▁the ▁method ▁to ▁give ▁meaning ful ▁results . ▁The ▁choice ▁of ▁the ▁applicable ▁framework ▁depends ▁mostly ▁on ▁the ▁nature ▁of ▁data ▁in ▁hand , ▁and ▁on ▁the ▁inference ▁task ▁which ▁has ▁to ▁be ▁performed . ▁ ▁One ▁of ▁the ▁lines ▁of ▁difference ▁in ▁interpretation ▁is ▁whether ▁to ▁treat ▁the ▁re gress ors ▁as ▁random ▁variables , ▁or ▁as ▁pre defined ▁constants . ▁In ▁the ▁first ▁case ▁( random ▁design ) ▁the ▁re gress ors ▁x i ▁are ▁random ▁and ▁sample d ▁together ▁with ▁the ▁y i ' s ▁from ▁some ▁population , ▁as ▁in ▁an ▁observ ational ▁study . ▁This ▁approach ▁allows ▁for ▁more ▁natural ▁study ▁of ▁the ▁asympt otic ▁properties ▁of ▁the ▁estim ators . ▁In ▁the ▁other ▁interpretation ▁( fixed ▁design ), ▁the ▁re gress ors ▁X ▁are ▁treated ▁as ▁known ▁constants ▁set ▁by ▁a ▁design , ▁and ▁y ▁is ▁sample d ▁condition ally ▁on ▁the ▁values ▁of ▁X ▁as ▁in ▁an ▁experiment . ▁For ▁practical ▁purposes , ▁this ▁distinction ▁is ▁often ▁un important , ▁since ▁estimation ▁and ▁inference ▁is ▁carried ▁out ▁while ▁condition ing ▁on ▁X . ▁All ▁results ▁stated ▁in ▁this ▁article ▁are ▁within ▁the ▁random ▁design ▁framework . ▁▁ ▁Class ical ▁linear ▁regression ▁model ▁ ▁The ▁classical ▁model ▁focus es ▁on ▁the ▁" finite ▁sample " ▁estimation ▁and ▁inference , ▁meaning ▁that ▁the ▁number ▁of ▁observations ▁n ▁is ▁fixed . ▁This ▁contrast s ▁with ▁the ▁other ▁approaches , ▁which