▁the ▁explan atory ▁variables ▁suggests ▁possible ▁heter os ced astic ity . ▁ ▁Res id uals ▁against ▁explan atory ▁variables ▁not ▁in ▁the ▁model . ▁Any ▁relation ▁of ▁the ▁resid uals ▁to ▁these ▁variables ▁would ▁suggest ▁considering ▁these ▁variables ▁for ▁inclusion ▁in ▁the ▁model . ▁ ▁Res id uals ▁against ▁the ▁fitted ▁values , ▁. ▁ ▁Res id uals ▁against ▁the ▁preceding ▁resid ual . ▁ ▁This ▁plot ▁may ▁identify ▁serial ▁correl ations ▁in ▁the ▁resid uals . ▁ ▁An ▁important ▁consideration ▁when ▁carrying ▁out ▁statistical ▁inference ▁using ▁regression ▁models ▁is ▁how ▁the ▁data ▁were ▁sample d . ▁ ▁In ▁this ▁example , ▁the ▁data ▁are ▁aver ages ▁rather ▁than ▁measurements ▁on ▁individual ▁women . ▁ ▁The ▁fit ▁of ▁the ▁model ▁is ▁very ▁good , ▁but ▁this ▁does ▁not ▁imply ▁that ▁the ▁weight ▁of ▁an ▁individual ▁woman ▁can ▁be ▁predicted ▁with ▁high ▁accuracy ▁based ▁only ▁on ▁her ▁height . ▁ ▁S ens itivity ▁to ▁round ing ▁ ▁This ▁example ▁also ▁demonstr ates ▁that ▁coefficients ▁determined ▁by ▁these ▁calculations ▁are ▁sensitive ▁to ▁how ▁the ▁data ▁is ▁prepared . ▁The ▁height s ▁were ▁originally ▁given ▁rounded ▁to ▁the ▁nearest ▁in ch ▁and ▁have ▁been ▁converted ▁and ▁rounded ▁to ▁the ▁nearest ▁cent imet re . ▁Since ▁the ▁conversion ▁factor ▁is ▁one ▁in ch ▁to ▁ 2 . 5 4   cm ▁this ▁is ▁not ▁an ▁exact ▁conversion . ▁The ▁original ▁inches ▁can ▁be ▁recovered ▁by ▁Round ( x / 0 . 0 2 5 4 ) ▁and ▁then ▁re - convert ed ▁to ▁metric ▁without ▁round