Commonality analysis (original) (raw)
Commonality analysis is a statistical technique within multiple linear regression that decomposes a model's R2 statistic (i.e., explained variance) by all independent variables on a dependent variable in a multiple linear regression model into commonality coefficients. These coefficients are variance components that are uniquely explained by each independent variable (i.e., unique effects), and variance components that are shared in each possible combination of the independent variables (i.e., common effects). These commonality coefficients sum up to the total variance explained (model R2) of all the independent variables on the dependent variable. Commonality analysis produces 2k − 1 commonality coefficients, where k is the number of the independent variables.
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| dbo:abstract | Commonality analysis is a statistical technique within multiple linear regression that decomposes a model's R2 statistic (i.e., explained variance) by all independent variables on a dependent variable in a multiple linear regression model into commonality coefficients. These coefficients are variance components that are uniquely explained by each independent variable (i.e., unique effects), and variance components that are shared in each possible combination of the independent variables (i.e., common effects). These commonality coefficients sum up to the total variance explained (model R2) of all the independent variables on the dependent variable. Commonality analysis produces 2k − 1 commonality coefficients, where k is the number of the independent variables. (en) |
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| dbo:wikiPageWikiLink | dbc:Econometric_modeling dbc:Regression_analysis dbr:SPSS dbr:Coefficient_of_determination dbr:Linear_regression dbr:Dependent_variable dbr:Independent_variable dbr:R_(programming_language) |
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| dct:subject | dbc:Econometric_modeling dbc:Regression_analysis |
| rdfs:comment | Commonality analysis is a statistical technique within multiple linear regression that decomposes a model's R2 statistic (i.e., explained variance) by all independent variables on a dependent variable in a multiple linear regression model into commonality coefficients. These coefficients are variance components that are uniquely explained by each independent variable (i.e., unique effects), and variance components that are shared in each possible combination of the independent variables (i.e., common effects). These commonality coefficients sum up to the total variance explained (model R2) of all the independent variables on the dependent variable. Commonality analysis produces 2k − 1 commonality coefficients, where k is the number of the independent variables. (en) |
| rdfs:label | Commonality analysis (en) |
| owl:sameAs | wikidata:Commonality analysis https://global.dbpedia.org/id/9qMxF |
| prov:wasDerivedFrom | wikipedia-en:Commonality_analysis?oldid=951302859&ns=0 |
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