Tag Archives: logit

Drivers of Certified Rice Seed Use in Kilombero District, Tanzania (Published)

The main objective of this paper is to assess the drivers of adoption of certified rice seeds among farmers in Kilombero District of Tanzania and as well assess the factors that influences the intensity of adoption of certified rice seeds. The random sampling technique was used to collect data from 130 rice farmers through in-person interview. The data were analyzed using two econometric techniques. Firstly, a binary logit model was used to identify factors influencing adoption of certified rice seeds. Secondly, Tobit regression was used to analyze the determinants of the extent of adoption of certified rice seeds. Empirical findings of this study show that factors such as marital status, access to land, membership of farm association and income from off-farm activities, significantly influenced the adoption of certified rice seeds while the factors that significantly influenced the extent of adoption of certified rice seeds in the study area include education level of the farmers, marital status and the farmer’s access to land. From a policy perspective, this study recommends that farmers should be assisted to improve on these factors because adoption of certified rice seed varieties is important for increasing agricultural productivity.

Keywords: Adoption, Probit, Tanzania, certified rice seeds, logit

Application of Logistic Regression Models for the Evaluation of Cholera Outbreak in Adamawa State Nigeria (Published)

Cholera outbreak occurred in four local government area of Adamawa state namely; Yola north, Yola south, Girei and Song between 11th may 2019 to 26th august 2019. WHO teams recorded 687 cholera patient whom received treatment at their various CTC with only 4 death as of the period of the outbreak. I explore cases of the disease outbreak, analyzed and estimate the parameters (demographic status and exploratory data) associated to the treatment outcome of the patients, identify the spread rate and targeted risk level using binary logistics regression. Our analysis has indicated that Yola North is the most affected area, majority of the patients are female and most of the respondent are children within the age group 1-14 years. The results depict that none of the demographic status was significantly associated with the mortality, similarly no significant association was observed for Culture status, Lab. Sample and Hospitalize status of the patients with mortality. However, the results of the association test between RDT status and mortality show that 2(0.3% of the total) who were RDT positive, were significantly associated with mortality. The binary logistic regression model estimations show that RDT, Culture, hospitalize status and LGA of the patients are the significant relationships at 10%, 10%, 5% and 10% levels respectively that determine the responses of patients to cholera treatment. Therefore, the findings depict that 99.4% of the patients “Alive” and 0.6% were “Dead”, this implies that the cholera treatment is very effective during the outbreak. Subsequently, it was deduced that the forecasting performance of the findings on both probit and logit regression model estimation from our analysis conform with the binary regression result.

Keywords: Epidemiology, Probit, RDT, binary regression, cholera, logit