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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/20583
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dc.contributor.advisorBankier, J. D.-
dc.contributor.authorWalpole, Ronald E.-
dc.date.accessioned2016-09-29T16:01:04Z-
dc.date.available2016-09-29T16:01:04Z-
dc.date.issued1955-10-
dc.identifier.urihttp://hdl.handle.net/11375/20583-
dc.description.abstract<p> In this thesis a systematic and short method for computing the expected values of mean squares has been developed. One chapter is devoted to the theory of regression analysis by the method of least squares using matrix notation and a proof is given that the method of least squares leads to an absolute minimum, a result which the author has not found in the literature. For two-way classifications the results have been developed for proportional frequencies, a subject which again has been neglected in the literature except for the Type II model. Finally, the methods for computing the expected values of the mean squares are applied to nested classifications and Latin square designs.</p>en_US
dc.language.isoen_USen_US
dc.subjectcomponents, variance analysis, theory of regression, matrixen_US
dc.titleComponents of Variance Analysisen_US
dc.typeThesisen_US
dc.contributor.departmentMathematicsen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Arts (MA)en_US
Appears in Collections:Digitized Open Access Dissertations and Theses

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