Biases in estimation and tests of significance in sequential multiple linear regression analysis
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This study is concerned with the underlying statistical theory required for tests of significance in sequential multiple linear regression analysis and with difficulties that can arise in developing valid tests. The relationship of the general linear subhypothesis theory to the Forward and Backward Selection Procedures is developed. The biases in estimation and the tests of significance are developed for the Forward Selection, Backward Elimination and Stagewise Regression procedures. The All Possible Regressions Procedure is described and a method for reducing the computational effort is outlined. Finally, the Stepwise Regression Procedure is considered and a Monte Carlo study of this procedure for the two variable case is discussed.
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This work is available on request. You can request a copy at https://library.carleton.ca/forms/request-pdf-copy-thesis
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Copyright © 1968 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 1968
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