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In statistics, Deming regression, named after W. Edwards Deming, is an errors-in-variables model that tries to find the line of best fit for a two-dimensional data set. It differs from the simple linear regression in that it accounts for errors in observations on both the x- and the y- axis.
It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation of the data is generically equivalent to the best, in the Frobenius norm, low-rank approximation of the data matrix. [1]
Deming found great inspiration in the work of Shewhart, the originator of the concepts of statistical control of processes and the related technical tool of the control chart, as Deming began to move toward the application of statistical methods to industrial production and management. Shewhart's idea of common and special causes of variation ...
Analyse-it Method Validation edition provides the standard Analyse-it statistical analyses above, plus procedures for method evaluation, validation and demonstration, including Bland–Altman bias plots, Linear regression, Weighted Linear regression, Deming regression, Weighted Deming regression and Passing Bablok for method comparison ...
Download as PDF; Printable version; ... Pages in category "Regression analysis" ... Deming regression; Dependent and independent variables;
Deming circle, an iterative management method; Deming regression, a 2D regression method that accounts for errors in both variables; Takming (德明), “Deming” in Cantonese pronunciation Takming (constituency)
I speculate that the Deming line is the average of the slopes and intercepts of the two regression lines that one gets when one calculates regression lines for the same X vs Y data upon reversing the axes (X vs Y, then Y vs X). But then, I am not sure. —Preceding unsigned comment added by Realusernamesareallused (talk • contribs) 21:05, 8 ...
Vertical distance: Simple linear regression; Resistance to outliers: Robust simple linear regression; Perpendicular distance: Orthogonal regression (this is not scale-invariant i.e. changing the measurement units leads to a different line.) Weighted geometric distance: Deming regression