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  2. Robust regression - Wikipedia

    en.wikipedia.org/wiki/Robust_regression

    The variable on the x axis is just the observation number as it appeared in the data set. Rousseeuw and Leroy (1986) contains many such plots. The horizontal reference lines are at 2 and −2, so that any observed scaled residual beyond these boundaries can be considered to be an outlier.

  3. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    If one runs a regression on some data, then the deviations of the dependent variable observations from the fitted function are the residuals. If the linear model is applicable, a scatterplot of residuals plotted against the independent variable should be random about zero with no trend to the residuals. [5]

  4. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    Figure 2. Noisy (roughly linear) data is fitted to a linear function and a polynomial function. Although the polynomial function is a perfect fit, the linear function can be expected to generalize better: If the two functions were used to extrapolate beyond the fitted data, the linear function should make better predictions. Figure 3.

  5. Statistical model validation - Wikipedia

    en.wikipedia.org/wiki/Statistical_model_validation

    Residual plots plot the difference between the actual data and the model's predictions: correlations in the residual plots may indicate a flaw in the model. Cross validation is a method of model validation that iteratively refits the model, each time leaving out just a small sample and comparing whether the samples left out are predicted by the ...

  6. 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.

  7. PRESS statistic - Wikipedia

    en.wikipedia.org/wiki/PRESS_statistic

    Models that are over-parameterised (over-fitted) would tend to give small residuals for observations included in the model-fitting but large residuals for observations that are excluded. The PRESS statistic has been extensively used in lazy learning and locally linear learning to speed-up the assessment and the selection of the neighbourhood size.

  8. Leverage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Leverage_(statistics)

    In a regression context, we combine leverage and influence functions to compute the degree to which estimated coefficients would change if we removed a single data point. Denoting the regression residuals as ^ = ^, one can compare the estimated coefficient ^ to the leave-one-out estimated coefficient ^ using the formula [6] [7]

  9. 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.