In statistics, unit-weighted regression is a simplified and robust version (Wainer & Thissen, 1976) of multiple regression analysis where only the intercept term is estimated. That is, it fits a model y ^ = f ^ ( x ) = b ^ + ∑ i x i , {\displaystyle {\hat {y}}={\hat {f}}(\mathbf {x} )={\hat {b}}+\sum _{i}x_{i},} where each of the x i {\displaystyle x_{i}} are binary variables, perhaps multiplied with an arbitrary weight.
In statistics, unit-weighted regression is a simplified and robust version (Wainer & Thissen, 1976) of multiple regression analysis where only the intercept term is estimated. That is, it fits a model y ^ = f ^ ( x ) = b ^ + ∑ i x i , {\displaystyle {\hat {y}}={\hat {f}}(\mathbf {x} )={\hat {b}}+\sum _{i}x_{i},} where each of the x i {\displaystyle x_{i}} are binary variables, perhaps multiplied with an arbitrary weight.
Source: Wikipedia "Unit-weighted regression" · CC BY-SA 4.0
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