Discriminant Modeling for Soil Attributes: Relationship between Sandy Soil Categories and Macronutrients | Chapter 1 | Research Advances and Challenges in Agricultural Sciences Vol. 3

In this study, discriminant analysis techniques were employed to investigate the relationship between sandy soil categories and macronutrients. Sandy soil has excellent drainage properties due to its coarse texture. It allows water to infiltrate quickly, preventing water logging and reducing the risk of root rot. The assumptions of linear discriminant function analysis, such as the normality of regressors, multicollinearity, and homoscedasticity were carefully examined. A parametric technique called discriminant analysis (DA) is used to identify the weightings of quantitative variables or predictors that best distinguish between two or more categories of dependent variables. The data were transformed using the Box-Cox method to improve normality, and a multivariate analysis of variance was employed to determine whether there were significant differences in soil macronutrients between the sand groups. The results showed that there were significant differences in soil macronutrients between the sand groups, and the classification accuracy of the discriminant function was 67%. The findings suggested that the discriminant function analysis could be used for classifying soil types based on their macronutrient content, particularly in sandy soil. By examining the discriminant weights assigned to each nutrient, and determining pH, EC and OM nutrients have the most significant impact on the sand categories. This information can be used to prioritize variables for further investigation.

Author(s) Details:

Rajarathinam A.,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli - 627 012, India.

Ramji M.,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli - 627 012, India.

Please see the link here: https://stm.bookpi.org/RACAS-V3/article/view/13261

Multicollinearity in Binary Logistic Regression Model | Chapter 2 | Theory and Practice of Mathematics and Computer Science Vol. 8

 One of the major issues with the binary logistic regression model is that the explanatory variables that are taken into account for the logistic regression model are strongly correlated. Multicollinearity affects confidence intervals and hypothesis tests by causing unstable estimates and inconsistent variances. The aim of this paper was to address some diagnostic metrics for detecting multicollinearity, such as tolerance and the Variance Inflation Factor. Variance proportions, condition index, and (VIF). Data from a survey of road injuries is used to demonstrate the adapted diagnostics. Secondary data for this analysis was obtained from the Traffic Police Headquarters in Colombo, Sri Lanka, from 2014 to 2016. The answer component is accident severity, which is divided into two categories: grave and non-grave. The correlation matrix, tolerance, and VIF are used to detect multicollinearity. The condition index and variance proportions were used to validate the values. Increase the sample size, remove one of the associated variables, and combine variables into an index are some of the logistic regression solutions available. It can be safely inferred that omitting one of the associated variables can significantly reduce multicollinearity without raising sample size. As a result, a stable and valid predictive logistic regression model can be developed based on adequate inspection and anti-multicollinearity steps.

Author(s) Details

Ms. N. A. M. R. Senaviratna
Department of Mathematics, The Open University of Sri Lanka, Sri Lanka.

Mr. T. M. J. A. Cooray
Department of Mathematics, University of Moratuwa, Sri Lanka.

View Book :- https://stm.bookpi.org/TPMCS-V8/issue/view/50