A standard curve is a graph relating a measured quantity (radioactivity, fluorescence, or optical density, for example) to concentration of the substance of interest in "known" samples.  You prepare and assay "known" samples containing the substance in amounts chosen to span the range of concentrations that you expect to find in the "unknown" samples.  You then draw the standard curve by plotting assayed quantity (on the Y axis) vs. concentration (on the X axis).  Such a curve can be used to determine concentrations of the substance in "unknown" samples.  This program automates this process. » Read more about Math Details

Math Details (Extracted from Curve fitting: Linear Least Squres)

The least-squares best fit for an x,y data set can be computed using only basic arithmetic. Here are the relevant equations for computing the slope and intercept of the first-order best-fit equation, y = intercept + slope*x, as well as the predicted standard deviation of the slope and intercept, and the coefficient of determination, R2, which is an indicator of the "goodness of fit". (R2 is 1.0000 if the fit is perfect and less than that if the fit is imperfect).

(In these equations, Σ represents summation; for example, Σx means the sum of all the x values, and Σx*y means the sum of all the x*y products, etc). The last two lines predict the standard deviation of the slope and intercept, based only on that data sample, assuming that the noise is normally distributed. These are estimates of the variability of slopes and intercepts you are likely to get if you repeated the data measurements over and over multiple times under the same conditions. Since the errors are random, they will be slightly different from time to time. The reliability of these standard deviation estimates depend on the number of data points in the curve fit; they improve with the square root of the number of points.