Principle of marginality (original) (raw)
In statistics, the principle of marginality is the fact that the average (or main) effects, of variables in an analysis are marginal to their interaction effect—that is, the main effect of one explanatory variable captures the effect of that variable averaged over all values of a second explanatory variable whose value influences the first variable's effect. The principle of marginality implies that, in general, it is wrong to test, estimate, or interpret main effects of explanatory variables where the variables interact or, similarly, to model interaction effects but delete main effects that are marginal tothem. While such models are interpretable, they lack applicability, as they ignore the dependence of a variable's effect upon another variable's value.
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| dbo:abstract | In statistics, the principle of marginality is the fact that the average (or main) effects, of variables in an analysis are marginal to their interaction effect—that is, the main effect of one explanatory variable captures the effect of that variable averaged over all values of a second explanatory variable whose value influences the first variable's effect. The principle of marginality implies that, in general, it is wrong to test, estimate, or interpret main effects of explanatory variables where the variables interact or, similarly, to model interaction effects but delete main effects that are marginal tothem. While such models are interpretable, they lack applicability, as they ignore the dependence of a variable's effect upon another variable's value. Nelder and Venables have argued strongly for the importance of this principle in regression analysis. (en) |
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| rdfs:comment | In statistics, the principle of marginality is the fact that the average (or main) effects, of variables in an analysis are marginal to their interaction effect—that is, the main effect of one explanatory variable captures the effect of that variable averaged over all values of a second explanatory variable whose value influences the first variable's effect. The principle of marginality implies that, in general, it is wrong to test, estimate, or interpret main effects of explanatory variables where the variables interact or, similarly, to model interaction effects but delete main effects that are marginal tothem. While such models are interpretable, they lack applicability, as they ignore the dependence of a variable's effect upon another variable's value. (en) |
| rdfs:label | Principle of marginality (en) |
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