Classical　least　squares　(CLS)　regression　is　a　popular　multivariate　statistical　method　used　frequently　for　quantitative　analysis　using　Fourier　transform　infrared　(FT-IR)　spectrometry.　Classical　least　squares　provides　the　best　unbiased　estimator　for　uncorrelated　residual　errors　with　zero　mean　and　equal　variance.　However,　the　noise　in　FT-IR　spectra,　which　accounts　for　a　large　portion　of　the　residual　errors,　is　heteroscedastic.　Thus,　if　this　noise　with　zero　mean　dominates　in　the　residual　errors,　the　weighted　least　squares　(WLS)　regression　method　described　in　this　paper　is　a　better　estimator　than　CLS.　However,　if　bias　errors,　such　as　the　residual　baseline　error,　are　significant,　WLS　may　perform　worse　than　CLS.　In　this　paper,　we　compare　the　effect　of　noise　and　bias　error　in　using　CLS　and　WLS　in　quantitative　analysis.　Results　indicated　that　for　wavenumbers　with　low　absorbance,　the　bias　error　significantly　affected　the　error,　such　that　the　performance　of　CLS　is　better　than　that　of　WLS.　However,　for　wavenumbers　with　high　absorbance,　the　noise　significantly　affected　the　error,　and　WLS　proves　to　be　better　than　CLS.　Thus,　we　propose　a　selective　weighted　least　squares　(SWLS)　regression　that　processes　data　with　different　wavenumbers　using　either　CLS　or　WLS　based　on　a　selection　criterion,　i.e.,　lower　or　higher　than　an　absorbance　threshold.　The　effects　of　various　factors　on　the　optimal　threshold　value　(OTV)　for　SWLS　have　been　studied　through　numerical　simulations.　These　studies　reported　that:　(1)　the　concentration　and　the　analyte　type　had　minimal　effect　on　OTV;　and　(2)　the　major　factor　that　influences　OTV　is　the　ratio　between　the　bias　error　and　the　standard　deviation　of　the　noise.　The　last　part　of　this　paper　is　dedicated　to　quantitative　analysis　of　methane　gas　spectra,　and　methane/toluene　mixtures　gas　spectra　as　measured　using　FT-IR　spectrometry　and　CLS,　WLS,　and　SWLS.　The　standard　error　of　prediction　(SEP),　bias　of　prediction　(bias),　and　the　residual　sum　of　squares　of　the　errors　(RSS)　from　the　three　quantitative　analyses　were　compared.　In　methane　gas　analysis,　SWLS　yielded　the　lowest　SEP　and　RSS　among　the　three　methods.　In　methane/toluene　mixture　gas　analysis,　a　modification　of　the　SWLS　has　been　presented　to　tackle　the　bias　error　from　other　components.　The　SWLS　without　modification　presents　the　lowest　SEP　in　all　cases　but　not　bias　and　RSS.　The　modification　of　SWLS　reduced　the　bias,　which　showed　a　lower　RSS　than　CLS,　especially　for　small　components.