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Pemenuhan Asumsi Klasik pada Data Beberapa Jenis Obat Anti Malaria terhadap Kasus Malaria Bulan Januari-Maret 2011 di Kabupaten Belu-Nusa Tenggara Timur

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The good results of hypothesis testing is testing that does not violate the four assumptions underlying the classical linear regression model. These assumptions include the assumption of normality of the error must be fulfilled, the assumption of freedom of error, error assumption and assuming a uniform spread of freedom between variables X. If all the assumptions are met then the classical estimators for parameters or variables OLS (Ordinary Least Squares) are BLUE (Best Linear Unbiassed Estimator) or in other words estimators obtained efficiently.

Reports on the results of data analysis Stock Usage Logistics Malaria in the district of Belu-East Nusa Tenggara in January until March 2011 declared the fulfillment of error normality assumption, and the assumption of error independence assumption of independence among variables X. But the uniform spread assumption that errors are not met . This causes the OLS estimators remain unbiased and consistent but there is no longer efficient in both small and large samples. Because the manifold is no longer a minimum, even if the sample is increased indefinitely.

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Call Number

Buletin Penelitian Kesehatan, 42 (4) : 257-266

Publisher

Jakarta : Sekretariat Balitbangkes., 2014

Collation

10p

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ISBN/ISSN

0125-9695

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Subject(s)

malaria
PHARMACEUTICAL PREPARATIONS

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