The　total　least　square　method　based　on　singular　value　decomposition　for　fitting　straight　line　and　plane　surface　has　been　developed　to　deal　with　the　straightness　calibration　problem.　Different　from　the　ordinary　least　square　method　only　taking　into　account　the　error　of　the　dependent　variable,　total　least　square　method　considers　the　errors　of　all　the　variables　in　a　symmetrical　way.　However,　in　practice,　it　is　difficult　to　choose　an　optimal　method　for　the　variable　errors　of　measurement　data　in　an　asymmetric　way.　This　article　presents　an　improved　calibration　method　for　straightness　error　of　a　coordinate　measuring　machine.　The　proposed　method,　named　as　improved　total　least　square,　could　fit　straight　line　and　plane　surface　when　the　variables　are　in　an　asymmetric　way.　In　improved　total　least　square　method,　weight　matrices　with　parameter　γ　set　between　the　independent　and　dependent　variables　are　introduced　to　augmented　matrix.　A　procedure　is　developed　to　determine　the　parameter　γ.　Numerical　cases　and　measurement　experiment　are　given　to　prove　the　performance　of　improved　total　least　square　method.　©　IMechE　2015.