Chapter 29 Weighting Data

return to the Course index
previous | next

Weighting Data

Student Objectives for this Chapter

To understand why all data are not equal
To understand measures of data accuracy and how they relate to weighting
To understand how to use different weighting schemes
To understand why different weighting schemes might be used
To understand the use of population parameter values in Nomograms
To understand Bayesian Estimation of pharmacokinetic parameter values

Not all data are the same. Not even data values within an experiment or set are the same. I don't mean the same value that is obvious but the uncertainty or error in the data. Each data point has a specified measured value but for that value to have any meaning we must also know the uncertainty or error in the data value. An estimate of the standard deviation or variance. In this way we have an estimate of the precision of the data value. When these data are modeled the measure of uncertainty should be included in the analysis. This uncertainty is expressed by means of a weight (or emphasis) given to each data point.

This page was last modified: Sunday, 28th Jul 2024 at 5:09 pm

Privacy Statement - 25 May 2018

iBook and pdf versions of this material and other PK material is available