Probability and Statistics for Engineers and Scientists.

Walpole, Ronald E.; Myers, Raymond H.

doi:10.2307/2284195

Cited by 728 publications

(1 citation statement)

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“…MTBF is a statistic. According to the large number theory, the arithmetic mean of samples, which is MTBF in our case tends to expectation of its distribution [2] when the sample size tends to infinite.…”

Section: Other Misconceptions In Mtbfmentioning

confidence: 91%

The Misconception and Risk of Using MTBF as a Reliability Indicator for Industry Management

Yuan¹,

Lu²

2022

Proceedings of the 2022 3rd International Conference on Management Science and Engineering Management (ICMSEM 2022)

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MTBF (Mean Time between Failure) is perhaps the most common indicator for reliability level used in the industries. This paper examines the statistical foundation of this indicator and investigates the limitation and risk of using it. Examples are provided to demonstrate the errors when misusing the MTBF. The examination is not trivial if the error is found significant. Some misconceptions on the MTBF are also discussed to raise the awareness of using this indicator correctly in the paper. Furthermore, the paper points out some results from the demonstration in the paper might be opposite to our intuition. Industrial engineers should avoid to make decision according to those unverified intuition. Upon the limitation and problems, the paper furthermore proposes some approaches to mitigate the limitation and the problem of the use of the MTBF.

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Section: Other Misconceptions In Mtbfmentioning

confidence: 91%

The Misconception and Risk of Using MTBF as a Reliability Indicator for Industry Management

Yuan¹,

Lu²

2022

Proceedings of the 2022 3rd International Conference on Management Science and Engineering Management (ICMSEM 2022)

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Daily Stress and Recurrence of Genital Herpes Simplex

Rand

Hoon²,

Massey³

et al. 1990

Arch Intern Med

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A linear algebra measure of cluster quality

Mather

2000

J. Am. Soc. Inf. Sci.

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One of the most common models in information retrieval (IR), the vector space model, represents a document set as a term-document matrix where each row corresponds to a term and each column corresponds to a document. Because of the use of matrices in IR, it is possible to apply linear algebra to this IR model. This paper describes an application of linear algebra to text clustering, namely, a metric for measuring cluster quality. The metric is based on the theory that cluster quality is proportional to the number of terms that are disjoint across the clusters. The metric compares the singular values of the term-document matrix to the singular values of the matrices for each of the clusters to determine the amount of overlap of the terms across clusters. Because the metric can be difficult to interpret, a standardization of the metric is defined, which specifies the number of standard deviations a clustering of a document set is from an average, random clustering of that document set. Empirical evidence shows that the standardized cluster metric correlates with clustered retrieval performance when comparing clustering algorithms or multiple parameters for the same clustering algorithm.

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Probability and Statistics for Engineers and Scientists.

Cited by 728 publications

References 1 publication

The Misconception and Risk of Using MTBF as a Reliability Indicator for Industry Management

The Misconception and Risk of Using MTBF as a Reliability Indicator for Industry Management

Daily Stress and Recurrence of Genital Herpes Simplex

A linear algebra measure of cluster quality

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