title: Error estimates on normal least squares linear regression with replicate injection of calibration standards.
abstract: BACKGROUND
  In bioanalysis, including replicate injection of calibration standards, a normal least squares linear regression model can be generated from, A: the first injection dataset; B: the second injection dataset; C: averaged data from both injection datasets; and D: all data from both injections. Sample results, and their estimated confidence intervals, are expected to be different among these four models.
  
  
  RESULTS
  Models C and D yield same slopes and intercepts, which are the mathematical means of respective values in models A and B. Relatively narrower confidence intervals on sample results are estimated in both models C and D as the former reduces the overall standard error of the curve and the latter increases the total number of calibration points and leads to a lower Student's t-value.
  
  
  CONCLUSIONS
  Replicate injection of calibration standards provides added benefits for an analytical measurement on instrument sensitivity compensation and relatively improved precision of results.
authors.affiliation: MPI Research, 3058 Research Drive, State College, PA 16801, USA.chaoyang.huang@ mpiresearch.com
authors.forename: Chaoyang
authors.initials: C
authors.lastname: Huang
authors.forename: Jennifer
authors.initials: J
authors.lastname: Ammerman
authors.forename: Paul
authors.initials: P
authors.lastname: Connolly
authors.forename: Pat
authors.initials: P
authors.lastname: de Lisio
authors.forename: Daniel
authors.initials: D
authors.lastname: Wright
mesh_terms: D000465:Algorithms; D002138:Calibration; D016001:Confidence Intervals; D016018:Least-Squares Analysis; D016014:Linear Models; D013058:Mass Spectrometry / Q000592:standards* / Q000706:statistics & numerical data*