Level: Advanced
The JMP 16 Design of Experiments platform includes new features that incorporate detection limits into column properties and are automatically applied during modeling with Generalized Regression in JMP Pro. We will demonstrate these new features and discuss how detection limits impact your data analysis.
When looking for very small amounts of a substance, there are limitations depending on the technology you are using. The limit of detection is when a substance is present but not detectable, but this can be extremely important when detecting small amounts of a substance in critical situations such as identifying the beginning of an infection or correctly identifying small amounts of an impurity.
Ignoring detection limits in data analysis has serious implications, including biasing and misleading basic statistics such as means and confidence intervals, and data modeling under incorrect assumptions about detection limits, which in turn lead to erroneous models. These detection limits have implications for industrial applications such as pharmaceutical and chemical manufacturing, analytical chemistry, and diagnostic test design.
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