Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis
In Regression analysis, an F test can be viewed as a comparison between a full and a restricted model. The most general F formula compares the error sums of squares (SSE’s) of these two models. This F formula is always correct because the SSE comparison is meaningful in all tests. Other formulas u...
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10361-5402019-09-29T05:46:21Z Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis Rahman, Mohammad Lutfur Coefficient of determination Full model Linear model Reparametrization Restricted model. In Regression analysis, an F test can be viewed as a comparison between a full and a restricted model. The most general F formula compares the error sums of squares (SSE’s) of these two models. This F formula is always correct because the SSE comparison is meaningful in all tests. Other formulas use the corrected model sum of squares (SSM) or the coefficient of determination (R2) to compare the full and restricted models. This article gives several examples where the SSM’s or R2’s of the two models cannot be compared, and hence where the use of F formulas based on SSM or R2 would be incorrect. This problem usually arises in tests of nonhomogeneous hypotheses, although it may also appear in other situation. 2010-10-18T05:46:07Z 2010-10-18T05:46:07Z 2005 Article http://hdl.handle.net/10361/540 en BRAC University Journal, BRAC University;Vol.2, No.2,pp. 35-38 application/pdf BRAC University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Coefficient of determination Full model Linear model Reparametrization Restricted model. |
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Coefficient of determination Full model Linear model Reparametrization Restricted model. Rahman, Mohammad Lutfur Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| description |
In Regression analysis, an F test can be viewed as a comparison between a full and a restricted
model. The most general F formula compares the error sums of squares (SSE’s) of these two
models. This F formula is always correct because the SSE comparison is meaningful in all tests. Other formulas use the corrected model sum of squares (SSM) or the coefficient of determination (R2) to compare the full and restricted models. This article gives several examples where the SSM’s
or R2’s of the two models cannot be compared, and hence where the use of F formulas based on SSM or R2 would be incorrect. This problem usually arises in tests of nonhomogeneous hypotheses, although it may also appear in other situation. |
| format |
Article |
| author |
Rahman, Mohammad Lutfur |
| author_facet |
Rahman, Mohammad Lutfur |
| author_sort |
Rahman, Mohammad Lutfur |
| title |
Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| title_short |
Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| title_full |
Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| title_fullStr |
Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| title_full_unstemmed |
Incorrect F-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| title_sort |
incorrect f-statistic to test nonhomogeneous hypothesis in bivariate regression analysis |
| publisher |
BRAC University |
| publishDate |
2010 |
| url |
http://hdl.handle.net/10361/540 |
| work_keys_str_mv |
AT rahmanmohammadlutfur incorrectfstatistictotestnonhomogeneoushypothesisinbivariateregressionanalysis |
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1814308038313508864 |