ing . ▁If ▁this ▁is ▁done ▁the ▁results ▁become : ▁ ▁Using ▁either ▁of ▁these ▁equations ▁to ▁predict ▁the ▁weight ▁of ▁a ▁ 5 ' ▁ 6 " ▁( 1 . 6 7 6 4 m ) ▁woman ▁gives ▁similar ▁values : ▁ 6 2 . 9 4   kg ▁with ▁round ing ▁vs . ▁ 6 2 . 9 8   kg ▁without ▁round ing . ▁Thus ▁a ▁seem ingly ▁small ▁variation ▁in ▁the ▁data ▁has ▁a ▁real ▁effect ▁on ▁the ▁coefficients ▁but ▁a ▁small ▁effect ▁on ▁the ▁results ▁of ▁the ▁equation . ▁ ▁While ▁this ▁may ▁look ▁innoc uous ▁in ▁the ▁middle ▁of ▁the ▁data ▁range ▁it ▁could ▁become ▁significant ▁at ▁the ▁extrem es ▁or ▁in ▁the ▁case ▁where ▁the ▁fitted ▁model ▁is ▁used ▁to ▁project ▁outside ▁the ▁data ▁range ▁( ext rap olation ). ▁ ▁This ▁highlight s ▁a ▁common ▁error : ▁this ▁example ▁is ▁an ▁ab use ▁of ▁O LS ▁which ▁inher ently ▁requires ▁that ▁the ▁errors ▁in ▁the ▁independent ▁variable ▁( in ▁this ▁case ▁height ) ▁are ▁zero ▁or ▁at ▁least ▁neg lig ible . ▁The ▁initial ▁round ing ▁to ▁nearest ▁in ch ▁plus ▁any ▁actual ▁measurement ▁errors ▁const itute ▁a ▁finite ▁and ▁non - neg lig ible ▁error . ▁As ▁a ▁result , ▁the ▁fitted ▁parameters ▁are ▁not ▁the ▁best ▁estimates ▁they ▁are ▁pres umed ▁to ▁be . ▁Though ▁not ▁totally ▁sp ur ious ▁the ▁error ▁in ▁the ▁estimation ▁will ▁depend ▁upon ▁relative ▁size ▁of ▁the ▁x ▁and ▁y ▁errors . ▁▁ ▁Another ▁example ▁with ▁less ▁real