The JMP 16 DOE platform adds the ability to incorporate detection limits into column attributes, which can be automatically applied during modeling using the JMP Pro Generalized Regression platform. In this presentation, Laura will demonstrate this new capability and discuss the impact of detection limits on data analysis.
When we are looking for very small amounts of a substance, we are often limited by the technology we are using. There may be something out there that we just cannot detect. This is called the limit of detection and is a strong guide for detecting low levels of a substance in critical situations, such as determining the onset of an infection over time or correctly identifying a small amount of an impurity. Ignoring the limit of detection in data analysis can have serious consequences, with basic statistics such as means and confidence intervals becoming biased and misleading. Modeling the data using incorrect assumptions based on the limit of detection can also lead to incorrect models. Limits of detection are used in a wide range of industrial applications such as pharmaceuticals, chemical manufacturing, analytical chemistry, and diagnostic test design.
