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  2. Regression validation - Wikipedia

    en.wikipedia.org/wiki/Regression_validation

    An illustrative plot of a fit to data (green curve in top panel, data in red) plus a plot of residuals: red points in bottom plot. Dashed curve in bottom panel is a straight line fit to the residuals. If the functional form is correct then there should be little or no trend to the residuals - as seen here.

  3. Partial regression plot - Wikipedia

    en.wikipedia.org/wiki/Partial_regression_plot

    The residuals from the least squares linear fit to this plot are identical to the residuals from the least squares fit of the original model (Y against all the independent variables including Xi). The influences of individual data values on the estimation of a coefficient are easy to see in this plot.

  4. Partial residual plot - Wikipedia

    en.wikipedia.org/wiki/Partial_residual_plot

    In applied statistics, a partial residual plot is a graphical technique that attempts to show the relationship between a given independent variable and the response variable given that other independent variables are also in the model.

  5. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals .

  6. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  7. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    A plot of the absolute or squared residuals versus the predicted values (or each predictor) can also be examined for a trend or curvature. Formal tests can also be used; see Heteroscedasticity. The presence of heteroscedasticity will result in an overall "average" estimate of variance being used instead of one that takes into account the true ...

  8. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    These deviations are called residuals when the calculations are performed over the data sample that was used for estimation (and are therefore always in reference to an estimate) and are called errors (or prediction errors) when computed out-of-sample (aka on the full set, referencing a true value rather than an estimate). The RMSD serves to ...

  9. Generalized additive model - Wikipedia

    en.wikipedia.org/wiki/Generalized_additive_model

    This will usually involve plotting the standardized residuals against fitted values and covariates to look for mean-variance problems or missing pattern, and may also involve examining Correlograms (ACFs) and/or Variograms of the residuals to check for violation of independence. If the model mean-variance relationship is correct then scaled ...