YEAR DESIGN I has generating class AGE*SEX*YEAR Note: For saturated models 500 has been added to all observed cells This value may be changed by using the Criteria= dELta subcommand The Iterative Proportional Fit algorithm converged at iteration 1 The maximum difference between observed and fitted marginal totals is 000 and the convergence criterion is. 278 系统以全饱和模型为起始,故显示各变量的实际例数、期望例数、残差和标准化残差, 因期望例数与实际例数相同,进而残差、标准化残差均为0 Observed, Expected Frequencies and Residuals Factor Code OBS count EXP count Residual Std Resid AGE YEAR 55.5 55.5 YEAR 43.5 43.5 YEAR 89.5 89.5 YEAR 140.5 140.5 00 YEAR 17.5 YEAR YEAR 20.5 YEAR 41.5 AGE SEX YEAR 1 165.5 165.5 00 YEAR 101.5 101.5 YEAR 3 104.5 104.5 00 YEAR 137.5 37 SEX YEAR 260.5 260.5 YEAR 2 233.5 233.5 0 YEAR 202.5 202.5 YEAR 278.5 278.5 AGE YEARYEAR 4 DESIGN 1 has generating class AGE*SEX*YEAR Note: For saturated models .500 has been added to all observed cells. This value may be changed by using the CRITERIA = DELTA subcommand. The Iterative Proportional Fit algorithm converged at iteration 1. The maximum difference between observed and fitted marginal totals is .000 and the convergence criterion is .278 系统以全饱和模型为起始，故显示各变量的实际例数、期望例数、残差和标准化残差， 因期望例数与实际例数相同，进而残差、标准化残差均为 0。 Observed, Expected Frequencies and Residuals. Factor Code OBS count EXP count Residual Std Resid AGE 1 SEX 1 YEAR 1 55.5 55.5 .00 .00 YEAR 2 43.5 43.5 .00 .00 YEAR 3 89.5 89.5 .00 .00 YEAR 4 140.5 140.5 .00 .00 SEX 2 YEAR 1 17.5 17.5 .00 .00 YEAR 2 9.5 9.5 .00 .00 YEAR 3 20.5 20.5 .00 .00 YEAR 4 41.5 41.5 .00 .00 AGE 2 SEX 1 YEAR 1 165.5 165.5 .00 .00 YEAR 2 101.5 101.5 .00 .00 YEAR 3 104.5 104.5 .00 .00 YEAR 4 137.5 137.5 .00 .00 SEX 2 YEAR 1 260.5 260.5 .00 .00 YEAR 2 233.5 233.5 .00 .00 YEAR 3 202.5 202.5 .00 .00 YEAR 4 278.5 278.5 .00 .00 AGE 3 SEX 1 YEAR 1 50.5 50.5 .00 .00