About: Proportionate reduction of error

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Proportionate reduction of error (PRE) is the gain in precision of predicting dependent variable from knowing the independent variable (or a collection of multiple variables). It is a goodness of fit measure of statistical models, and forms the mathematical basis for several correlation coefficients. The summary statistics is particularly useful and popular when used to evaluate models where the dependent variable is binary, taking on values {0,1}.

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  • Proportionale Fehlerreduktionsmaße (proportionale Fehlerreduktion (PFR) englisch proportionate reduction of error, kurz: PRE, daher auch PRE-Maße) geben indirekt die Stärke des Zusammenhangs zwischen zwei Variablen und an. (de)
  • Proportionate reduction of error (PRE) is the gain in precision of predicting dependent variable from knowing the independent variable (or a collection of multiple variables). It is a goodness of fit measure of statistical models, and forms the mathematical basis for several correlation coefficients. The summary statistics is particularly useful and popular when used to evaluate models where the dependent variable is binary, taking on values {0,1}. (en)
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  • Proportionale Fehlerreduktionsmaße (proportionale Fehlerreduktion (PFR) englisch proportionate reduction of error, kurz: PRE, daher auch PRE-Maße) geben indirekt die Stärke des Zusammenhangs zwischen zwei Variablen und an. (de)
  • Proportionate reduction of error (PRE) is the gain in precision of predicting dependent variable from knowing the independent variable (or a collection of multiple variables). It is a goodness of fit measure of statistical models, and forms the mathematical basis for several correlation coefficients. The summary statistics is particularly useful and popular when used to evaluate models where the dependent variable is binary, taking on values {0,1}. (en)
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  • Proportionale Fehlerreduktionsmaße (de)
  • Proportionate reduction of error (en)
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