Quantitative analysis of samples using 3rd order polynomial regression. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and 3rd order polynomial regression is applied to these points. The concentrations of the unknown samples are determined from the fit. To avoid ambiguous results (which can occur from turning points) extrapolation is not used with this method, i.e. concentration values will only be found within the range of the standards.

Linearized quantification of sample concentrations for enzyme immunoassay (EIA). This uses the linearized method which plots logit B/B0 versus log concentration using a linear fit. Sample positions with %B/B0 values greater than 80% or less than 20% are highlighted in yellow. These samples should be re-assayed as they generally fall out of the linear range of the standard curve.

Quantitative analysis of samples using quadratic regression. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and quadratic regression is applied to these points. The concentrations of the unknown samples are determined from the fit. To avoid ambiguous results extrapolation is not used with this method, i.e. concentration values will only be found within the range of the standards.

Quantitative analysis of samples using a Five Parameter Logistic Fit (5PL) suitable for asymmetric sigmoidal data. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and a Five Parameter Logistic Fit (5PL) is made through these points. The concentrations of the unknown samples are determined from the fit. It is important to note that concentrations can only be determined for samples which fall within the range of the determined upper and lower asymptotes of the fit (the a and d parameters).

Quantitative analysis of samples using a Michaelis-Menten fit. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and a Michaelis-Menten fit is applied to these points. The concentrations of the unknown samples are determined from the fit. The maximum rate (or Vmax) is determined from the resulting fit, this equals the asymptote. Concentrations are determined for samples outside the range of the standards using the fit.

Quantitative analysis of samples using an exponential fit. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and an exponential fit is applied to these points. The concentrations of the unknown samples are determined from the fit. Concentrations are determined for samples outside the range of the standards using the fit.

Quantitative analysis of samples using a Cubic Spline. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and a Cubic Spline is applied to these points. The concentrations of the unknown samples are determined from the fit. For measurements outside the range of the standards a linear extrapolation is used.

Quantitative analysis of samples using linear regression. All samples are first corrected by the mean of the blank group measurements. The standard data points are plotted (concentration vs. corrected measurement) and linear regression is applied to these points. The concentrations of the unknown samples are determined from the fit.

Calculates the mean, %CV and standard deviation of samples grouped according to the Microplate Layout.

Subtracts the mean of the blank measurements from the unknown samples and calculates the %CV.