Publications by [R]eliability
Gaussian Process Regression for FEA Designed Experiments – Building the Basics in R
A Google search for ‘Gaussian Process Regression’ returns some intimidating material for a non-statistician. After filtering away the obscure stuff I’ll never understand and digging around within the code that makes GPR happen, I’m proud to say that I feel I’ve gotten my arms around the basics of GPR. The only way to confirm if this is ...
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Power Analysis for a DV Test – Frequentist and Bayesian Estimation in R
When testing is costly or resource intensive, it’s not uncommon for management to ask an engineer “what are the chances that we pass?”. The answer will depend on the team’s collection of domain knowledge around the product and requirement but also in how the question is interpreted. It will also be sensitive to sample size considerations....
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Learning from Failure – Nitinol Fracture Mechanics in R
Despite our best efforts, nitinol implants fracture and fail. Sometimes we want them to fail (on the bench, to learn). Other times they fail unexpectedly and we need find out why. When the failure is a fractured nitinol structural element, high magnification imaging of the fracture surface via optical microscopy and SEM is essential. A trained en...
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Permutation Test for NHST of 2 Samples in R
As engineers, it is not uncommon to be asked to determine whether or not two different configurations of a product perform the same. Perhaps we are asked to compare the durability of a next-generation prototype to the current generation. Sometimes we are testing the flexibility of our device versus a competitor for marketing purposes. Maybe we id...
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Assessing Design Verification Risk with Bayesian Estimation in R
Suppose our team is preparing to freeze a new implant design. In order to move into the next phase of the PDP, it is common to perform a suite of formal “Design Freeze” testing. If the results of the Design Freeze testing are acceptable, the project can advance from Design Freeze (DF) into Design Verification (DV). DV is an expensive and reso...
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Stopping Rules for Significance Testing in R
When doing comparative testing it can be tempting to stop when we see the result that we hoped for. In the case of null hypothesis significance testing (NHST), the desired outcome is often a p-value of < .05. In the medical device industry, bench top testing can cost a lot of money. Why not just recalculate the p-value after every test and stop w...
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Modeling Particulate Counts as a Poisson Process in R
I’ve never really worked much with Poisson data and wanted to get my hands dirty. I thought that for this project I might combine a Poisson data set with the simple Bayesian methods that I’ve explored before since it turns out the Poisson rate parameter lambda also has a nice conjugate prior (more on that later). Poisson distributed data are ...
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Heart Disease Prediction From Patient Data in R
In this post I’ll be attempting to leverage the parsnip package in R to run through some straightforward predictive analytics/machine learning. Parsnip provides a flexible and consistent interface to apply common regression and classification algorithms in R. I’ll be working with the Cleveland Clinic Heart Disease dataset which contains 13 va...
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Confounders and Colliders – Modeling Spurious Correlations in R
Like many engineers, my first models were based on Designed Experiments in the tradition of Cox and Montgomery. I hadn’t seen anything like a causal diagram until I picked the The Book of Why which explores all sorts of experimental relationships and structures I never imagined.1 Colliders, confounders, causal diagrams, M-bias – these concept...
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Creating and Using a Simple, Bayesian Linear Model (in brms and R)
This post is my good-faith effort to create a simple linear model using the Bayesian framework and workflow described by Richard McElreath in his Statistical Rethinking book.1 As always – please view this post through the lens of the eager student and not the learned master. I did my best to check my work, but it’s entirely possible that some...
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