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This paper proposes a robust economic-statistical design for Hotelling's T2 control chart to monitor linear profiles, accounting for uncertain parameters. Unlike previous studies assuming fixed inputs, this work applies an uncertainty interval approach to address parameter uncertainty when investigating the T2 chart design. In this way the challenge of parameter uncertainty's adverse impact on the effectiveness of quality control models in practice is addressed. The key contribution of this study is formulating a T2 control chart that maintains accuracy despite uncertainty. Results demonstrate the value of increased costs to mitigate uncertainty risks. The proposed approach also facilitates selecting appropriate protection levels against uncertainty. The paper examines practical challenges and applications of implementing the T2 chart under real-world conditions affected by uncertainty. Overall, the proposed robust T2 control chart design incorporates parameter ranges for monitoring linear profiles, contrasting with previous models assuming fixed parameters. [ABSTRACT FROM AUTHOR]

Although statistical literacy has become a key competence in today's data-driven society, it is usually not a part of statistics education. To address this issue, we propose an innovative concept for a conference-like seminar on the topic of statistical literacy. This seminar draws attention to the relevance and importance of statistical literacy, and moreover, students are made aware of the process of science communication and are introduced to the peer review process for the assessment of scientific papers. In the summer term 2020, the seminar was conducted as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. In this article, we present the concept of the seminar and our experience with this concept in the summer term 2020. [ABSTRACT FROM AUTHOR]

In this article, we propose the generalized one-tailed hypergeometric test with fuzzy hypotheses for both test methods: significance testing (by controlling the type I error) and hypothesis testing (by controlling both error types). With regard to the fuzzy set theory, this test is the generalization of the conventional hypergeometric test for a proportion and provides an additional gradual consideration of the indifference zone in compliance with expert opinion or user priorities. Further, a practical application for one of the most promising resulting test types (right-tailed hypothesis testing) is carried out in statistical quality control. In particular, single sampling plans for attributes are determined via an algorithm based on the Fibonacci sequence. In addition, a parametric sensitivity analysis of calculated sampling plans is carried out in relation to changes of the lot size, consumer's and producer's risks, as well as of specified target values of quality levels. Moreover, the impact of various shapes of membership functions (piecewise linear, convex, concave, and s-shaped) is investigated by comparing respective sampling plans. [ABSTRACT FROM AUTHOR]