JMP’s Generalized Regression platform offers researchers much flexibility in choosing the most appropriate model. This paper contributes to the literature showing that Poisson regression proves useful for historical epidemiology. We find that our results largely correspond with Pearl’s conclusions but contain some additional nuances that are substantive. We use Poisson forward selection on the variables in the platform with AICc validation. In this presentation, we revisit Pearl’s data and apply variable selection with a pseudo-variable in JMP®'s Generalized Regression platform instead of Pearl’s correlation analysis. Using partial correlation coefficients, he tried to isolate the net effects of the possible explanatory factors, such as general demographic characteristics of the cities and death rates for various diseases, on the variables measuring the severity of the epidemic. He explored the factors that might explain the epidemic’s explosiveness and destructiveness in America’s largest cities. In 19, Raymond Pearl published four empirical studies on the Spanish flu epidemic. This gives insight into risk analyses, e.g., how much I should budget to account for gauge errors, whether (or how much) to spend on improving our gauge, etc.
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In this paper, we extend the learning and script functionality as we discuss how to combine gauge characteristics with the costs of individually passing a failed part, rejecting a good part, and projecting production volumes. These errors cost real money! But how do we quantify those costs? This paper builds on the results shown in a 2022 JMP Americas Discovery paper (2022-US-30MP-1123) that discussed how to quantify the gauge performance and how to set “informative manufacturing specs” (or guardbands) to improve the gauge’s performance in segregating good vs. The mistake likelihood is higher for parts that lie near the specification limits. Large or small, the errors lead to gauges having some likelihood of making Type 1 and Type 2 errors (passing a bad part or failing a good part). They might be minuscule, or they might be large, but they always exist. We can now use equivalence testing and relate it to the individual variable contributions.Īll gauges have errors. The multivariate platform in JMP helps create a holistic picture of the process for each time point. JMP scripting is a great way to automate the data prep, visualisations, and production of all the plots and comparison tests for those data sets. In addition, many processes change over time, and we are interested in capturing whether they behave similarly across the duration of the process. The challenge with equivalence testing is that it requires the scientists to provide a value for what they deem an acceptable difference between the groups of data. When we look at a few parameters, those techniques are easy to apply, but when we have large numbers of variables, it becomes difficult to see the bigger picture. Comparison techniques such as t-tests and ANOVAs are widespread, but equivalence testing has become a standard way to show two processes behave similarly. Working as a manufacturer in the biopharmaceutical industry means we often need to show that we obtain similar results on different sites when transferring a manufacturing process and, notably, when scaling processes up or down.
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