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Data mining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD). The models created using these algorithms could be evaluated against appropriate metrics to verify the model’s credibility. Regression.
Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. Because individual observations have so little information, statistical significance remains important to assess.
Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. We can now pass the preprocessed data to the Pumas NCAReport function, which calculates a wide range of relevant NCA metrics. We can merge all the metrics in a separate DataFrame for further analysis.
In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.
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