Adjusted means (based on Section 7.2): Let Y 1 represent the (‘‘unadjusted’’) mean prestige score of professional occupations in the Canadian occupational prestige data, Y 2 that of white-collar...

Adjusted means (based on Section 7.2): Let Y¹
represent the (‘‘unadjusted’’) mean prestige score of professional occupations in the Canadian occupational prestige data, Y²
that of white-collar occupations, and Y³
that of blue-collar occupations. Differences among the Y^j
may partly reflect differences among occupational types in their income and education levels. In the dummy-variable regression in Equation 7.7, type-of-occupation differences are ‘‘controlled’’for income and education, producing the fitted regression equation

Consequently, if we fix income and education at particular values—say, X₁
= x₁
and X₂
= x=— then the fitted prestige scores for the several occupational types are given by (treating ‘‘blue collar’’as the baseline type):