The ordinary Least Squares method is considered as one of the most important way of estimating the parameters of the general linear model because of it's ease and simplicity and because of rationality of the results obtained when the specific assumptions are achieved regarding the general linear model . One of these assumptions is that the value of the error term in time is independent on its own preceding value or values E(Ut Ut-s) = 0 s ?0 if this assumption does not hold then we have problem of autocorrelation . The other assumption is that the explanatory variables in the model are orthogonal [R(x) = p+1 < n ] if this assumption does not hold then we have problem of multicollinearity. In this book we will try to discuss these two problems simultaneously.
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Date of Birth: Jan 1, 1969 Nationality: Sudan Current position: Assistant Professor Assistant Professor, Department of Mathematics-Faculty of Science- Taibah University. (April 2010 - Present). Assistant Professor, Department of Statistics and Computer - Faculty of Science -Shendi University (Aug. 2005 - March. 2010).
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The ordinary Least Squares method is considered as one of the most important way of estimating the parameters of the general linear model because of it's ease and simplicity and because of rationality of the results obtained when the specific assumptions are achieved regarding the general linear model . One of these assumptions is that the value of the error term in time is independent on its own preceding value or values E(Ut Ut-s) = 0 s 0 if this assumption does not hold then we have problem of autocorrelation . The other assumption is that the explanatory variables in the model are orthogonal [R(x) = p+1 n ] if this assumption does not hold then we have problem of multicollinearity. In this book we will try to discuss these two problems simultaneously. 200 pp. Englisch. Bestandsnummer des Verkäufers 9783844324761
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Eledum HusseinDate of Birth: Jan 1, 1969 Nationality: Sudan Current position: Assistant Professor Assistant Professor, Department of Mathematics-Faculty of Science- Taibah University. (April 2010 - Present). Assistant Professor,. Bestandsnummer des Verkäufers 5472894
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Taschenbuch. Zustand: Neu. Biased Estimation Methods with Autocorrelation using Simulation | Problem of Multicoolinearity and Autocorrelation | Hussein Eledum | Taschenbuch | 200 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844324761 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 107021634
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The ordinary Least Squares method is considered as one of the most important way of estimating the parameters of the general linear model because of it''s ease and simplicity and because of rationality of the results obtained when the specific assumptions are achieved regarding the general linear model . One of these assumptions is that the value of the error term in time is independent on its own preceding value or values E(Ut Ut-s) = 0 s ¿0 if this assumption does not hold then we have problem of autocorrelation . The other assumption is that the explanatory variables in the model are orthogonal [R(x) = p+1VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 200 pp. Englisch. Bestandsnummer des Verkäufers 9783844324761
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The ordinary Least Squares method is considered as one of the most important way of estimating the parameters of the general linear model because of it's ease and simplicity and because of rationality of the results obtained when the specific assumptions are achieved regarding the general linear model . One of these assumptions is that the value of the error term in time is independent on its own preceding value or values E(Ut Ut-s) = 0 s 0 if this assumption does not hold then we have problem of autocorrelation . The other assumption is that the explanatory variables in the model are orthogonal [R(x) = p+1 n ] if this assumption does not hold then we have problem of multicollinearity. In this book we will try to discuss these two problems simultaneously. Bestandsnummer des Verkäufers 9783844324761
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