Modeling for Soil Parameters Based on Multinomial Logistic Regression | Chapter 3 | Research Updates in Mathematics and Computer Science Vol. 3

 This study aimed to investigate the pH associated with micronutrients in soil samples. The characterization of the soil involved analyzing various factors, including pH, Sulfur, Zinc, Iron, Copper, Manganese, and Boron. A total of 500 soil samples were collected and categorized into four pH ranges: moderately acidic (5.1-6.0%), slightly acidic (6.1-6.5%), neutral (6.6-7.5%), and slightly alkaline (7.6-8.5%). The pH levels were neutral, indicating an ideal condition for maximum availability of primary nutrients essential for plant growth. The collected data was subjected to multinomial logistic regression and multivariate linear regression analyses. A formula was derived to determine the soil micronutrients based on the corresponding pH levels. The statistical tests of significance using linear regression indicated significant differences (P>0.05) between the pH values of the soil samples for Sulfur, Manganese, and Boron. Additionally, correlation analysis explored the relationships among different soil parameters. The likelihood ratio test supported a relationship between pH and micronutrient levels. The findings demonstrated that pH levels can be a predictive indicator of micronutrient performance in soil. Model evaluation, including the goodness of fit tests and pseudo-R-squares, accounted for 17% of the overall assessment. Moreover, the Multinomial logistic regression analysis achieved a classification accuracy of 64% for predicting pH levels. These findings have important implications for effective soil management strategies, aiding in optimizing nutrient availability for plant growth.

Author(s) Details:

Rajarathinam A.,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli – 627 012, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/RUMCS-V3/article/view/13981

Determination of Vaccine Efficacy in Anti-Leprosy Vaccination Trial: A Regression Model Analysis | Chapter 7 | Current Innovations in Disease and Health Research Vol. 3

 This member compares the estimation of vaccine efficiency between the unoriginal method, logistic model, and Cox regression model in consideration of improve belief of vaccine efficacy a suggestion of correction using unoriginal methods. It also aims to recognize additional doing factors in evaluating cure efficacy among differing vaccines. Vaccination plays a vital act in eradicating and controlling the expansive spread of diseases like smallpox, polio, measles, nervous system infection and leprosy throughout the world. In a potential cohort study 1,71,400 things    were enrolled and they were confirmed as athletic with routine healing checkups. Various regression models such as Cox reversion, binomial regression, Poisson reversion model have been applied to find the bettering in the estimation of Vaccine Efficacy. The participants were made inquiries through two subsequent surveys accompanying two to three year interval. The reversion models were built utilizing the individuals' demographic and dispassionate information. We were capable to calculate the odds percentages from the regression models and estimate the regulated relative risk as well as vaccine influence.   Estimated relative risks obtained   from Logistic regression and Poisson reversion   models give comparable results in second resurvey. By comparing  the estimated cure efficacy utilizing conventional methods accompanying  regression models the Vaccines ICRC and BCG+Kml have maximum guardianship and Logistic regression models provides appropriate results than the Cox reversion model.  Vaccine ICRC and BCG+Kml gives better protection results grazing between 63 and 76 than vaccines BCG and M.w, which have cure efficacy 'tween 24 and 35 in second resurvey.

Author(s) Details:

A. Venmani,
Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur – 603203, Chengalpattu District, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/CIDHR-V3/article/view/11354