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Besides English proficiency, intercultural communicative competence is a desirable qualification for flight attendants. An under-researched cultural group of Muslim passengers was the focus of this mixed-method study which investigated Thai flight attendants' intercultural sensitivity and related biographical details. Topics in intercultural communication between Muslim passengers and Thai flight attendants were explored. Data collected from 416 self-rated questionnaires on intercultural sensitivity were completed by a purposively selected sample and were analyzed using chi-square test and frequency count. The interview data from a total of 45 Muslim passengers as well as both Muslim and non-Muslim Thai flight attendants were analyzed based on content analysis. The findings showed that most Thai flight attendants rated themselves with a high score in intercultural sensitivity. The factors significantly associated with intercultural sensitivity were sex, work duration, job position, and being ex-crew at the significance level of 0.05. Qualitative findings suggested that three major topics, which were the concept of halal, religious practices, and some additional topics, should be considered in training on intercultural communication with Muslim passengers through English language teaching (ELT). Implications for both airlines and pedagogy are discussed in this paper.

In this paper, the unit Garima distribution is introduced. It is used for analysing proportional data. Some statistical properties of the proposed distribution are investigated, including survival and hazard functions, order statistics, quantile function, and stress-strength reliability measures. A new family of continuous distributions, called the unit Garima-generated family of distributions, is also provided. It used the unit Garima distribution as the main generator. Some sub-models of the unit Garima-generated family of distributions are provided, such as the unit Garima-beta, unit Garima-Weibull, and unit Garima-normal distributions. The method of maximum likelihood is used to estimate the model parameters. A Monte Carlo simulation is used to illustrate the performance of the percentile confidence interval construction for each parameter of the proposed distributions. Finally, the developed distributions are applied to eight real data sets. [ABSTRACT FROM AUTHOR]

This article investigates the logarithmic interval estimation of a ratio of two binomial proportions in dependent samples. Previous studies suggest that the confidence intervals of the difference between two correlated proportions and their ratio typically do not possess closed-form solutions. Moreover, the computation process is complex and often based on a maximum likelihood estimator, which is a biased estimator of the ratio. We look at the data from two dependent samples and explore the general problem of estimating the ratio of two proportions. Each sample is obtained in the framework of direct binomial sampling. Our goal is to demonstrate that the normal approximation for the estimation of the ratio is reliable for the construction of a confidence interval. The main characteristics of confidence estimators will be investigated by a Monte Carlo simulation. We also provide recommendations for applying the asymptotic logarithmic interval. The estimations of the coverage probability, average width, standard deviation of interval width, and index H are presented as the criteria of our judgment. The simulation studies indicate that the proposed interval performs well based on the aforementioned criteria. Finally, the confidence intervals are illustrated with three real data examples. [ABSTRACT FROM AUTHOR]

The characteristic of count data that have a high frequency of zeros and ones can be considered under a zero-one inflated distribution. In this article, we present a zero-one inflated negative binomial - beta exponential distribution to analyze for such data. This distribution shows that it is extended the mixture negative binomial with beta exponential distributions, was proposed by Pudprommarat, Bodhisuwan, and Zeephongsekul (2012). Some important properties of this distribution are discussed, which include probability mass function, moment generating function, moment about the origin, mean and variance. Additionally, some sub-models are presented. Its parameters are also derived based on the maximum likelihood estimation procedure. The applicability of the proposed distribution is demonstrated for fitting to three real data sets. We also evaluate the abilities of model selection relying on the negative log-likelihood, Akaike information criterion, mean absolute error, root mean squared error, discrete Kolmogorov–Smirnov test and Anderson-Darling tests. Results from this study indicate the zero-one inflated negative binomial - beta exponential distribution has shown the best fit for these data sets when it is compared with some sub-models and the zero-one inflated of traditional distributions. [ABSTRACT FROM AUTHOR]

A new truncated distribution, called the truncated power Lomax (TPL) distribution, is proposed. This is a truncated version of the power Lomax distribution. The TPL distribution has increasing and decreasing shapes of the hazard function. Some statistical properties, such as moments, survival, hazard, and quantile functions, are discussed. The maximum likelihood estimation (MLE) is constructed for estimating the unknown parameters of the TPL distribution. Moreover, the distribution has been fitted with real data sets to illustrate the usefulness of the proposed distribution. From the results of the example applications, the TPL distribution provides a consistently better fit than the other distributions, i.e., power Lomax and Lomax. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a new linear regression model for count data, namely generalized-Poisson Lindley (GPL) linear model. The GPL linear model is performed by applying generalized linear model to GPL distribution. The model parameters are estimated by the maximum likelihood estimation. We utilize the GPL linear model to fit two real data sets and compare it with the Poisson, negative binomial (NB) and Poisson-weighted exponential (P-WE) models for count data. It is found that the GPL linear model can fit over-dispersed count data, and it shows the highest log-likelihood, the smallest AIC and BIC values. As a consequence, the linear regression model from the GPL distribution is a valuable alternative model to the Poisson, NB, and P-WE models. [ABSTRACT FROM PUBLISHER]

The aims of this paper are to introduce and validate a new distribution, which is related to the hyperbolic function. The differential equation is applied to obtain the survival and probabilistic functions. The moment generating function is provided by using the integral transform. The proposed distribution is applied to real data sets. It is demonstrated that it can be used as an alternative model in various disciplines such as electronics, financial, weather, and arrival times. [ABSTRACT FROM AUTHOR]

In this paper, a new framework for generating lifetime distributions is introduced, called the Topp-Leone generated (TLG) family of distributions. The generator of the TLG family is the Topp-Leone distribution, which was proposed by Topp and Leone in 1955. Some various properties of the TLG distribution are discussed, e.g., survival function, hazard function, moments and generating function. In addition, the TLG family is capable of improving fitted results and tail behavior of existing distributions. We present the Topp-Leone generalized exponential (TLGE) distribution as an example of the TLG distribution. Some graphical representations related to the probability density function and hazard function of the TLGE distribution are provided. In application study, the goodness of fit test based on the TLGE, the generalized exponential (GE), and exponentiated generalized exponential (EGE) distributions are compared. The results emphasize that the TLGE distribution can be considered as a competitive distribution for the GE and EGE distributions. [ABSTRACT FROM AUTHOR]