linear regression analysis (least-squares analysis, ordinary regression analysis)

A statistical technique for estimating the best linear relationship between two variables. The estimated line has the property that the sum of the squares of the deviations from the line is a minimum, hence the name least-squares analysis. This statistical technique is commonly applied to the data from a comparison of methods experiment, taking the test method values as the y-variable and the comparison method values as the xvariable. The statistics calculated usually include the slope (b), y-intercept (a), and standard deviation about the regression line, also termed the standard error of the regression line (sy/x) and also called the standard deviation of residuals (sres). These statistics provide information about the proportional, constant, and random errors between the methods, respectively.