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Anaemia is a severe worldwide health issue that greatly affects children, young girls, and women, especially those who are pregnant. Anaemia burden continues to be a major problem in Akwa Ibom State despite constant efforts by the government, which has made large financial investments in the health sector, especially in the area of making healthcare delivery accessible and affordable. This study aimed at creating an appropriate time series model for the studied series while investigating the government’s intervention in the prevalence of paediatric anaemia. The dataset, is the monthly clinical cases of childhood anaemia spanning from January 1997 to 2011, and ARIMA intervention analysis is the statistical technique employed. A total of 9,413 children aged 0 to 14 years were affected, with the highest recorded cases of 147. The ARIMA (1,1,1) model is fitted and adjudged adequate for the dataset in the pre-intervention period. The significant impact parameter with a value of 88.3963 implies that the intervention was not successful in producing the intended result, as indicated by the sign of the impact parameter. It is also evidenced in the number of hospital admission cases, which increased in the post-intervention period, with the highest case number of 145 compared to the period before the intervention. The impact of the intervention was instant, as indicated by the delay parameter, with a growth rate of 0.

KEYWORDS: Intervention analysis, Pulse function, Anaemia prevalence, Time series model.

Based on a random sample, the relationship between two continuous variables is depicted by a scatter plot of the bivariate data (x, y). If the relation is deemed linear, then the direction and strength of the linear association is measured by Pearson’s product moment correlation coefficient r, which is computed using a calculator or software. Although r is routinely calculated in many quantitative disciplines, most users are unskilled at guessing the magnitude of the correlation from the scatter plot alone.

To overcome this wide-spread deficiency, we propose two alterations to a scatter plot: (1) control its aspect ratio, and (2) superimpose a coverage ellipse. These two simple changes reveal to the viewer not only the correlation, but also the two regression lines, the coefficient of determination and any potential outlier.

KEYWORDS: linear regression model, bivariate normal distribution, least-squares method, circumscribe, coverage ellipse.

Dear readers, to get the most out of this paper, please read it linearly from start to finish, and guess the correlation in each Figure as and when we ask you to.

We discuss the Gamma L´evy process, including path properties, the inverse process, integrability, and its spin-offs obtained by compounding, exponentiation, and other operations; further extendable to arbitrary sigma-finite continuous Borel spaces. An appendix on modular spaces and deterministic jump processes is included.

KEYWORDS: Gamma Levy process, moments, simulation, martingale fields.

This paper discusses the measures of poverty such as incidence, depth and severity and Gini Coefficient of inequality for rural Odisha by different regions and different social groups using data from 61st round (2004-05), 66th round (2009-10) and 68th round (2011-12) of National Sample Survey (NSS) household consumer expenditure survey. The study has been undertaken by existing the incidence of poverty, depth and severity of poverty by using simple head count method as well as fitting a log linear regression estimation on Monthly Per Capita Expenditure (MPCE). It is observed that the incidence, depth and severity were more in the Southern region than the Northern and the Coastal region during 2004-05 and 2011-12 as per the estimates. The study also shows wide disparities in poverty among different regions as well as social groups of rural Odisha. In case of social groups, the Scheduled Tribes (ST) communities are the most vulnerable group in poverty incidence, depth and severity than Scheduled Castes (SC), Other Backward Classes (OBC) and other social groups. Besides, log linear regression model has been fitted to the observed consumer expenditure taking logarithm of MPCE as dependent variable and a host of auxiliary variables supposed to be associated MPCE. A combined estimate of incidence of poverty across social groups has been obtained by combining both direct and predicted percentage of persons below poverty line.

KEYWORDS: Poverty, FGT, National Sample Survey, Incidence, Depth, Severity and Kappa.

In this present study, we have introduced a novel three-parameter distribution (NTPD) designed for modeling lifetime data, showcasing its remarkable performance in reliability and survival analysis. We have presented expressions for a range of statistical functions, including the probability density function, distribution function, survival function, quantile function, hazard rate function, reversed hazard rate function, cumulative hazard rate function, skewness, and kurtosis. Visual representations of the probability density and hazard rate curves have also been provided. To assess the suitability and effectiveness of our proposed model, we employed a COVID-19 second wave dataset from Nepal. We estimated the model parameters using three different techniques: maximum likelihood, least squares, and Cramer’s-von Mises. To confirm the model’s validity, we employed a range of statistical criteria, such as Akaike’s Information Criterion, Bayesian Information Criterion, Corrected Akaike’s Information Criterion, and Hannan-Quinn Information Criterion. Additionally, P-P 
we performed the Kolmogorov-Smirnov, Anderson-Darling, and Cramer-von Mises tests. These tests were carried out to determine the adequacy of the fit for our data. Our empirical findings demonstrate that, when compared to alternative lifetime distributions, the suggested distribution exhibits superior fitting and greater flexibility for lifetime data analysis. The utilization of the R programming language facilitated robust and insightful data analysis, leading to valuable insights.

KEYWORDS: Bayesian Information Criterion, COVID-19, Goodness of fit, Maximum Likelihood Estimation, novel three-parameter distribution, second wave, survival function.

We formulate three generalized Bayesian models for analyzing interrater and intrarater reliability in the presence of multilevel data. Stan implementations of these models provide new estimates of interrater and intrarater reliability. We also derive formulas for calculating marginal correlations under each of the three models. Comparisons of the kappa estimates and marginal correlations across the different models are presented from two real-world datasets. Simulations demonstrate properties of the different measures of agreement under different model assumptions.

KEYWORDS: Reliability; Bayesian; Hierarchical; Nested; Stan