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Statistics vocabulary · Regression and Correlation

Residual

Meaning in statistics, with examples

/rɪˈzɪdʒuəl/nounCommon term

In statistics, residual means: The difference between an observed value and the value a model predicts for that observation; it shows how far the model misses each data point.

In Turkish: artık, kalıntı, artık değer

How it is used

Residuals are plotted and inspected to check regression assumptions: they should look like random noise with a constant spread. Patterns, funnels or curves in a residual plot suggest that the model is missing something. Also used as an adjective: residual variance, residual plot.

Example sentences

  1. The residuals fan out as income increases, which suggests heteroscedasticity.

  2. Plot the residuals against the fitted values before you interpret the coefficients.

Common collocations

  • residual plot
  • plot the residuals
  • standardised residuals
  • residual variance
  • large residual
  • regressionA family of methods for modelling how an outcome variable changes with one or more predictor variables, used both to explain relationships and to make predictions.
  • linear regressionA regression method that models the outcome as a straight-line function of one or more predictors, estimating an intercept and one slope per predictor.
  • least squaresA method of fitting a line or model by choosing the parameter values that make the sum of the squared residuals as small as possible.
  • assumptionA condition that must hold, at least approximately, for a statistical test or model to give valid results, such as normality, equal variances or independent observations.
  • outlierAn observation that lies unusually far from most of the other values in a data set; it may come from an error or from a genuinely unusual case.

More terms: Regression and Correlation

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