Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/10859
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dc.contributor.authorBusari, A. F-
dc.contributor.authorUsman, A-
dc.contributor.authorCole, A.T-
dc.date.accessioned2021-07-21T14:19:00Z-
dc.date.available2021-07-21T14:19:00Z-
dc.date.issued2010-
dc.identifier.citationISSN 1680-5194en_US
dc.identifier.issn1680-5194-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/10859-
dc.descriptionNAen_US
dc.description.abstractAbstract: As difficult as it can be to determine the plant attribute that contributes most to better yield of cereal crop named Maiwa. We use multivariate regression model to determine the contribution of Plant height (X ); 1 Number of leaves (X ); Number of tillers (X ) and Leaf’s area in square feet (X ). Four multivariate regression 2 3 4 models were developed by dropping each attribute. A data set collected from the Institute of Agricultural Research (IAR) Ahmadu Bello University, Samaru-Zaria was used for the analysis. Using each of the models to assess the contribution of each attribute, it was discovered that the Multivariate regression model that has the best fits of the data set, when covariates are dropped one after the other is Y = 0.02371 - 0.003111X + 2 0.001759X - 0.002503X . Thus, plant height (X ) is an irrelevant plant attribute for the variety-Maiwa.en_US
dc.description.sponsorshipNAen_US
dc.publisherPakistan Journal of Nutritionen_US
dc.relation.ispartofseriesAsian Network for Scientific Information;-
dc.subjectKey words: Regressor, predictor, goodness-of-fit, multivariate, yield, tilleren_US
dc.titleConstructing the Best Regression Model for Maiwa Varietyen_US
dc.typeArticleen_US
Appears in Collections:Statistics

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