Constructing the Best Regression Model for Maiwa Variety
DOI:
https://doi.org/10.3923/pjn.2010.380.386Keywords:
Goodness-of-fit, multivariate, predictor, regressor, tiller, yieldAbstract
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 (X1); Number of leaves (X2); Number of tillers (X3) and Leaf’s area in square feet (X4). Four multivariate regression 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 - 0.003111X2 + 0.001759X3 - 0.002503X4. Thus, plant height (X1) is an irrelevant plant attribute for the variety-Maiwa.
Downloads
Published
Issue
Section
License
Copyright (c) 2010 Asian Network for Scientific Information
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
