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2009•RePEc: Research Papers in EconomicsRequires access

A Review Of Student Test Properties In Condition Of Multifactorial Linear Regression

Florin Marius Pavelescu

Open publisher page 6 citations

Abstract

Having in view previous contributions of the author related to the impact of collinearity on the estimated values of parameters of multifactorial linear regressions, in this paper the correlation between Student and Fisher test is emphasized and a correction of the standard computation of the Student test is proposed, in order to increase the respective test relevance and to detect the occurrence of “statistical illusions” determined by collinearity. Also, the impact of adding a new explanatory variable in the linear regression equation is analyzed and the conditions in which such a step is efficient are determined.

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What this paper is about

Having in view previous contributions of the author related to the impact of collinearity on the estimated values of parameters of multifactorial linear regressions, in this paper the correlation between Student and Fisher test is emphasized and a correction of the standard computation of the Student test is proposed, in order to increase the respective test relevance and to detect the occurrence of “statistical illusions” determined by collinearity. Also, the impact of adding a new explanatory variable in the linear regression equation is analyzed and the conditions in which such a step is efficient are determined.

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Available abstract

Having in view previous contributions of the author related to the impact of collinearity on the estimated values of parameters of multifactorial linear regressions, in this paper the correlation between Student and Fisher test is emphasized and a correction of the standard computation of the Student test is proposed, in order to increase the respective test relevance and to detect the occurrence of “statistical illusions” determined by collinearity. Also, the impact of adding a new explanatory variable in the linear regression equation is analyzed and the conditions in which such a step is efficient are determined.

Key concepts: Collinearity, Linear regression, Econometrics, Test (biology), Regression analysis, Statistics, Mathematics, Regression

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