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Abstract:Like other developing countries, Bangladesh is struggling to meet the food needs for its population. There are two possible solutions to this problem-import of food products and increasing national food production. In Bangladesh, increase in agricultural production is hindered by various constraints inherent in the sector such as low availability of cultivable land, declining fertility of soil, pest and virus attack problems etc. Chemical fertilizer and pesticide are vital inputs for agricultural production. With the growing popularity of modern agriculture, fertilizer and pesticide consumption in Bangladesh has been increasing over the years. The main objective of this study was to estimate the determinants of adoption decision of high doses of chemical inputs in agriculture. Using the Bivariate probit model, this study has made it possible to identify the key determinants affecting the adoption decision of chemical inputs use. The results indicate that age, education, farming experience, total family income, training, and extension service are the main determinants for the adoption decision of high doses of chemical inputs in agriculture. Farmers' education and total family income are positively related to the adoption of chemical inputs whereas age, farming experience and training are negatively related.

<p style="text-align:justify">Suspension and expulsion are adversely related to negative outcomes of students, such as falling behind academically, an increased risk of absenteeism or dropout from schools. Suspension discrepancy due to ethnicity is evident and well known in the United States. The proper understanding of factors affecting suspension may lead to intervention towards the reduction of suspension episodes in the schools. The aim of this study is to determine how student, parent and school characteristics affect the likelihood of K-8 school students’ suspension in the United States. We analyze the National Household Education Surveys of 2019 with a sample of 9,699 K-8 students to evaluate the risk factors of suspension. The study finds that 6% students receive K-8 school suspensions. Bivariate analysis suggests that gender, ethnicity, poverty, parental education, school type, repeated grades, contacted for behavioral problem and school type are significantly associated with the K-8 students’ suspension. An adjusted analysis of these factors via multiple logistic regression suggests that the odds of suspension of NH-black students are 2.7 times the odds of NH-white students. Odds of suspension for students with parental education below HS is 3.2 (95% CI: 1.77-5.80) compared those with parental education at Graduate or professional level. Likewise, students of public schools have higher odds of suspension compared to private schools. There is significant evidence that students with repeated grades, poor parents, school type and those contacted for behavioral problems have substantially higher odds of suspension.</p>

<p style="text-align:justify">Absenteeism is of great concern for K–12 school students in the United States. The aim of this study is to evaluate effects of parental participation types in absenteeism of Elementary and Secondary Education (K-12) students in the United States. We analyze the data of the U.S. Department of Education (Hanson et al., 2019), in relation to students, schools and parents’ characteristics, along with various parental involvement activities, for exploring how these factors influence K-12 students’ absenteeism in the United States. We employ Chi-square tests for the significance of relationships between parental involvement types and absenteeism of K-12 students. We also undertake multiple logistic regression analyses to evaluate the significance and odds of K-12 students’ absenteeism due to parental involvement activities and other underlying factors. The results of bivariate analyses suggest that parental involvement types are significantly associated with K-12 absenteeism (chi-squared p-value<0.05). Multiple logistic regression analysis reveals that only a subset of underlying parental activities is significantly related to higher odds of absenteeism as measured by estimates of odds ratios (OR) and 95% confidence interval estimates. It also suggests that parental education, ethnicity and poverty adjusted for other factors also significantly affect absenteeism.</p>

A new estimator of the Poisson parameter is proposed using the moment generating function. Some statistical properties of the proposed estimator are studied. The performance of the new estimator is compared with the maximum likelihood estimator (MLE) via examples and simulation in terms of goodness of fit and relative efficiency. Simulation and examples to real-life data suggest that the new estimator has higher relative efficiency compared to the MLE, while both are comparable in goodness of fit. The R program utilized in all computation and simulation is incorporated to facilitate the implementation of the new estimator in computation.