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O Ciclo de Seminários do Departamento de Estatística (DEST) da UFRN apresenta uma série de 3 seminários ministrada pelo professor Geoff Vining do Departamento de Estatística da Virgínia Tech.
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<div>Esta série de seminários é intitulada "Understanding Weibull Regression and Its Application in Reliability" e tem como objetivo apresentar a regressão de Weibull e sua aplicação em estudos de confiabilidade.</div>
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<div> A regressão de Weibull é extremamente importante para testes de vida acelerados em que o mecanismo de falha assumido é “falha devido ao elo mais fraco”. A palestra final resume um projeto da NASA que estudou as taxas de falha esperadas de equipamentos
comumente usados em espaçonaves.</div>
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<div>Os seminários ocorrerão nos dias 20 e 27 de junho e 05 de julho de 2022 no auditório do CCET da UFRN e serão transmitidos ao vivo pelo canal do YouTube do DEST UFRN</div>
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<a href="https://youtube.com/channel/UCr_8R_aiS69hroG4-Yvzv4Q" id="LPlnkOWALinkPreview">https://youtube.com/channel/UCr_8R_aiS69hroG4-Yvzv4Q</a><br>
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Resumo:</div>
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<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt">This seminar series consists of three approximately one hour lectures
in English introducing Weibull regression and its application within reliability studies. Weibull regression is extremely important for accelerated life tests where the assumed failure mechanism is “failure due to the weakest link.” The final lecture summarizes
an actual NASA study that studied the expected failure rates of composite overwrapped pressure vessels very commonly used on spacecraft.
</span><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt">The first lecture provides the origins and the foundations for the standard
two-parameter Weibull distribution. It derives the distribution and its various forms, in particular the hazard function, which is extremely important for explain the risk of failure for the system over time. It then presents a high level overview of estimation
and inference of the important parameters. It then introduces the concept of censoring.</span><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt">The second lecture covers two major topics. The first is the power law
model critical for accelerated life tests. Many systems have quite long expected lifetimes. As a result, engineers often “stress” the system to produce failures in a much shorter period of time. The power law is the most common empirical approach to such
testing. The second major topic is proper residual plots for assessing the assumption of the Weibull distribution. Some of this work appears to be novel.</span><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt">The third lecture summarizes the NASA study. It highlights the importance
of the proper analysis of the data, especially when dealing with engineers who do not understand proper data analysis. How does the data analyst deal with an engineer who has planned the perfect experiment to test everyone of her/his theories, and the data
contradict all of them?</span><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
<p class="MsoNormal" style="font-family:Calibri;margin-bottom:0,0000pt"><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt">Traditionally, mathematical statisticians tend to dominate the field of
statistical analysis of reliability data. These people often have little appreciation for linear models and their analyses. Power law models ultimately are linear models that require likelihood based estimation and inference. This series of lectures illustrates
how to combine the mathematical statistics and the linear models theory to enhance the understanding of the experimental results.</span><span style="mso-spacerun:'yes';font-family:'Times New Roman';mso-fareast-font-family:Calibri;font-size:12,0000pt"><o:p> </o:p></span></p>
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Atenciosamente,</div>
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Andressa N Siroky</div>
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Professora Adjunta do Departamento de Estatística da UFRN</div>
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Este canal foi criado para mostrar conteúdo sobre ESTATÍSTICA. Somos o Departamento de Estatística – DEST e integramos o Centro de Ciências Exatas e da Terra – CCET, na Universidade Federal do Rio Grande do Norte.</div>
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