Deming regression is a method of linear regression that finds a line of best fit for a set of related data. It differs from simple linear regression in that it accounts for error in both the x and the y-axis.

For instance, if the x-axis data is known to have no error, but the y data does, (such as a population estimate (y), at a known time (x)), simple linear regression would work.

If both sets of data contained error, for instance the relationship between concentrations of two substances in blood, Deming regression would be more appropriate.

The disadvantage with Deming regression, is that it is mathematically more complex to do. This means doing the calculations, either on paper, or by writing a formula for a spreadsheet, are more difficult.

The Deming regression is named after W. Edwards Deming.