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Testing hypotheses for differences between linear regression linesAuthor(s): Stanley J. Zarnoch
Source: Res. Note SRS-17. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. 20 p.
Publication Series: Research Note (RN)
Station: Southern Research Station
PDF: Download Publication (4.06 MB)
DescriptionFive hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise comparisons. Use of the Bonferroni adjustment to ensure a desired experimentwise type I error rate is emphasized. SAS software is used to illustrate application of these concepts to an artificial simulated dataset. The SAS code is provided for each of the five hypotheses and for the contrasts for the general test and all possible specific tests.
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CitationZarnoch, Stanley J. 2009. Testing hypotheses for differences between linear regression lines. Res. Note SRS-17. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. 20 p.
KeywordsContrasts, dummy variables, F-test, intercept, SAS, slope, test of conditional error
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