### 8 assays found tagged with linear regression

Calculates the concentration of samples from a calibration curve of the standards plotted against their absorbance values. The concentration for each well is calculated from the absorbance value, constants from the linear regression and the specified dilution factors. y-int and slope are obtained from the log-regression fit of the calibration data. Measurements outside the range of the standards are highlighted in yellow.

Calculation of Biotinidase Enzyme Activity from absorbance measurements made at 550nm. The substrate blank (well B1 in the default layout) is corrected by the reagent blank (well A1 in the default layout). The standards and the blanks are corrected by the reagent blank. The unknowns are corrected by their own blank which has also been corrected by the corrected substrate blank. The activity of each unknown is measured from the standard curve using linear regression.

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 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.

Quantitation of HIV-1 p24 core antigen (HIV-1 p24) in human serum or plasma and in cell culture supernatant. The ratio of the raw data measured at 490nm and 650nm is first calculated. These values are then blank corrected by the mean of the blank wells. The corrected values are plotted against the specified concentration values. The concentration of HIV-1 p24 for each sample is determined using linear regression.

Quantitative analysis of samples using linear regression on adjusted measurements. All samples are first corrected by the mean of the measurements of Standard1. The standard data points are plotted (concentration vs. adjusted measurement) and linear regression is applied to these points. The concentrations of the unknown samples are determined from the fit.

Quantitative measurement of the amount of an individual cytokine or chemokine using ELISA in multiple samples. This method subtracts the mean of the blank replicates (background absorbance) from all of the samples. The corrected absorbance is plotted against the standards concentrations on a log-log graph for a seven-point standard curve. Linear regression is used to find the best straight-line through the standards and used to determine the concentration of the unknowns. An optional dilution factor is applied.

Quantitative analysis of samples using linear regression. Standard data points are plotted (concentration vs. measurement) with linear regression applied to these points. The concentrations of the samples are determined from the line with any dilution factors applied. If the optional blank group is defined on the microplate layout, background correction is performed (the average of the blank replicates is subtracted from the raw measurement values). Samples outside the range of the standards are highlighted in yellow.