I certainly agree with Gareth that the ANCOVA approach should be simple. But it won't address all hypotheses posed by the "slopes and/or intercepts" framework. What you need is a model selection approach. Fit the various linear models of interest (some of which may be ANCOVA-type models), calculate AICc, and see which model(s) are best supported by the data. Oh, and check all the assumptions. This is a piece of cake in R; see e.g. the packages 'lm' and 'AICcmodavg'.
Dave Hewitt Research Fishery Biologist USGS Western Fisheries Research Center Klamath Falls Field Station, Oregon http://profile.usgs.gov/dhewitt From: Gareth Russell Subject: Re: Comparing slopes and intercepts in linear regressions I'm afraid ANCOVA is the way to go. It shouldn't be cumbersome though, not in any up-to-date software. If you have three columns of data (two continuous, one categorical), and specify one of the continuous as the dependent and the other two as predictors, then almost all software packages will do an ANCOVA. Gareth Russell ----- On Wed, 31 Mar 2010 11:02:07 -0400, Howie Neufeld wrote: Dear All - I have a stats question concerning comparing linear regressions. If you have two or more regressions, and want to know if their slopes and/or intercepts are significantly different, what procedure would you use? I am familiar with SAS mainly. Zar has a two-sample t-test equivalent for comparing two slopes, but the procedure for intercepts is extremely cumbersome, as is the multiple slope comparison, which involves ANOCOVA. Thanks! Howie Neufeld
