Treats linear regression diagnostics as a tool for application of
linear regression models to real-life data. Presentation makes
extensive use of examples to illustrate theory. Assesses the effect
of measurement errors on the estimated coefficients, which is not
accounted for in a standard least squares estimate but is important
where regression coefficients are used to apportion effects due to
different variables. Also assesses qualitatively and numerically
the robustness of the regression fit.
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