On one-sample Bayesian tests for the mean

Abdelrazeq, Ibrahim; Al-Labadi, Luai; Alzaatreh, Ayman

doi:10.1080/02331888.2020.1726918

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…To evaluate the impact of using the module on the changes in students' attitudes and entrepreneurial intentions, this research employed a t-test formula. The formula was used to test the deviation of two population means (Abdelrazeq et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

The impacts of using modules on students’ entrepreneurial attitudes and intentions

Wahidmurni

Pusposari

Nur

et al. 2022

CJES

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The covid-19 pandemic made classroom-based learning change into online-based digital learning. It leads to the students’ need for useful learning resources to study independently. The current research aimed to evaluate the impacts of module-assisted learning on students’ entrepreneurial attitudes and intentions. It was designed with a one-group quasi-experimental pre-and post-test, and the whole learning process was online. It was hypothesized that there will be a remarkable improvement in students’ entrepreneurial attitudes and intentions after they learn with the module. Data were collected by distributing questionnaires to students of two selected universities. To test the deviation between the two population means, t-test formula was used. The results demonstrated that 1) the use of modules can significantly improve students’ entrepreneurial attitudes and 2) it cannot significantly increase their entrepreneurial intentions. Online learning of entrepreneurship with modules can increase entrepreneurial attitudes, but to increase entrepreneurial intentions, it is vital to explore learning strategies through maximum use of information technology. Keywords: COVID-19 pandemic, entrepreneurial attitude, entrepreneurial intention, entrepreneurship education, entrepreneurship module.

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Section: Discussionmentioning

confidence: 99%

The impacts of using modules on students’ entrepreneurial attitudes and intentions

Wahidmurni

Pusposari

Nur

et al. 2022

CJES

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“…Use sample statistics to estimate the population by finding out the confidence interval for the difference between two population proportions [2]. In this method, generally make hypotheses and calculate confidence intervals to define whether the hypothesis is true or wrong.…”

Section: Methodology ⅰmentioning

confidence: 99%

An investigation into whether single nucleotide polymorphisms (Snps) of OPRM1 influence on opioid addiction

Guo

Yang²,

Zhang

2022

International Conference on Biomedical and Intelligent Systems (IC-BIS 2022)

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Opioid is one of the main components used in many pain pills, such as fentanyl. It can selectively inhibit the impulse transmission of some excitatory nerves by acting on opioid receptors in central nervous tissues and relieving pain. However, opioid is easy to be addicted. Opioid addiction has become a serious social issue. The main aim of this dissertation is to investigate the influences of Single Nucleotide Polymorphisms (SNPs) of OPRM1 on opioid addiction. In this essay, three methodologies are used to conclude whether SNPs of OPRM1 influence opioid addiction. The first method uses sample statistics to hypothesize and estimate the population by finding out the confidence interval for the difference between two population proportions by randomly choosing 500 European subjects and sequencing the OPRM1 SNPs. In the second method, an oxford nanopore is used to determine the specific sequence by creating a characteristic disruption in the electrical current. In the third method, house mice's zygotes are first developed in vitro. Then, house mice learn how to self-administer in the vapor chambers.

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“…The relative belief ratio was developed in [9], and it has become a widely used tool in the theory of Bayesian statistical hypothesis testing. For some references, see, for example, [11][12][13][14]. Suppose a statistical model is given by the density function { f θ (x) : θ ∈ Θ} with respect to Lebesgue measure on the parameter space Θ.…”

Section: Inferences Using Relative Beliefmentioning

confidence: 99%

A Bayesian One-Sample Test for Proportion

Al-Labadi

Cheng

Fazeli-Asl

et al. 2022

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This paper deals with a new Bayesian approach to the one-sample test for proportion. More specifically, let x=(x1,…,xn) be an independent random sample of size n from a Bernoulli distribution with an unknown parameter θ. For a fixed value θ0, the goal is to test the null hypothesis H0:θ=θ0 against all possible alternatives. The proposed approach is based on using the well-known formula of the Kullback–Leibler divergence between two binomial distributions chosen in a certain way. Then, the difference of the distance from a priori to a posteriori is compared through the relative belief ratio (a measure of evidence). Some theoretical properties of the method are developed. Examples and simulation results are included.

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On one-sample Bayesian tests for the mean

Cited by 13 publications

References 27 publications

The impacts of using modules on students’ entrepreneurial attitudes and intentions

The impacts of using modules on students’ entrepreneurial attitudes and intentions

An investigation into whether single nucleotide polymorphisms (Snps) of OPRM1 influence on opioid addiction

A Bayesian One-Sample Test for Proportion

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