General Linear Models Procedure Class Level Information Class Levels Values VARIETY 4 A B C D Number of observations in data set = 13 General Linear Models Procedure Dependent Variable: Y Sum of Mean Source DF Squares Square F Value Pr > F Model 3 0.81016026 0.27005342 4.79 0.0293 Error 9 0.50791667 0.05643519 Corrected Total 12 1.31807692 R-Square C.V. Root MSE Y Mean 0.614653 5.899316 0.2375609 4.0269231 Source DF Type I SS Mean Square F Value Pr > F VARIETY 3 0.81016026 0.27005342 4.79 0.0293 T for H0: Pr > |T| Std Error of Parameter Estimate Parameter=0 Estimate INTERCEPT 3.575000000 B 21.28 0.0001 0.16798093 VARIETY A 0.491666667 B 2.27 0.0496 0.21686245 B 0.350000000 B 1.70 0.1231 0.20573378 C 0.750000000 B 3.65 0.0054 0.20573378 D 0.000000000 B . . . NOTE: The X'X matrix has been found to be singular and a generalized inverse was used to solve the normal equations. Estimates followed by the letter 'B' are biased, and are not unique estimators of the parameters. General Linear Models Procedure Dependent Variable: Y T for H0: Pr > |T| Std Error of Parameter Estimate Parameter=0 Estimate grand mean 3.97291667 57.93 0.0001 0.06857793 variety A 4.06666667 29.65 0.0001 0.13715585 variety B 3.92500000 33.04 0.0001 0.11878045 variety C 4.32500000 36.41 0.0001 0.11878045 variety D 3.57500000 21.28 0.0001 0.16798093 Variance Components Estimation Procedure Class Level Information Class Levels Values VARIETY 4 A B C D Number of observations in data set = 13 MIVQUE(0) Variance Component Estimation Procedure SSQ Matrix Source VARIETY Error Y VARIETY 31.90532544 9.53846154 2.41896450 Error 9.53846154 12.00000000 1.31807692 Estimate Variance Component Y Var(VARIETY) 0.05637609 Var(Error) 0.06502798 Variance Components Estimation Procedure Class Level Information Class Levels Values VARIETY 4 A B C D Number of observations in data set = 13 Variance Components Estimation Procedure Dependent Variable: Y Source DF Type I SS Type I MS VARIETY 3 0.81016026 0.27005342 Error 9 0.50791667 0.05643519 Corrected Total 12 1.31807692 Source Expected Mean Square VARIETY Var(Error) + 3.1795 Var(VARIETY) Error Var(Error) Variance Component Estimate Var(VARIETY) 0.06718638 Var(Error) 0.05643519 Variance Components Estimation Procedure Class Level Information Class Levels Values VARIETY 4 A B C D Number of observations in data set = 13 Maximum Likelihood Variance Components Estimation Procedure Dependent Variable: Y Iteration Objective Var(VARIETY) Var(Error) 0 -31.92433980 0.04955371 0.05715858 1 -31.92477941 0.04867054 0.05745195 2 -31.92478562 0.04856610 0.05748720 3 -31.92478570 0.04855258 0.05749177 4 -31.92478570 0.04855258 0.05749177 Convergence criteria met. Asymptotic Covariance Matrix of Estimates Var(VARIETY) Var(Error) Var(VARIETY) 0.0025465318 -.0003091998 Var(Error) -.0003091998 0.0007583373 Variance Components Estimation Procedure Class Level Information Class Levels Values VARIETY 4 A B C D Number of observations in data set = 13 Restricted Maximum Likelihood Variance Components Estimation Procedure Dependent Variable: Y Iteration Objective Var(VARIETY) Var(Error) 0 -29.45631598 0.05368318 0.06192179 1 -29.55289652 0.07300627 0.05703190 2 -29.55290079 0.07314420 0.05700515 3 -29.55290081 0.07315542 0.05700298 4 -29.55290081 0.07315542 0.05700298 Convergence criteria met. Asymptotic Covariance Matrix of Estimates Var(VARIETY) Var(Error) Var(VARIETY) 0.0060870419 -.0003104904 Var(Error) -.0003104904 0.0007361706 The MIXED Procedure Class Level Information Class Levels Values VARIETY 4 A B C D REML Estimation Iteration History Iteration Evaluations Objective Criterion 0 1 -11.93984487 1 2 -14.97773308 0.00122963 2 1 -14.98774873 0.00002663 3 1 -14.98795135 0.00000001 4 1 -14.98795145 0.00000000 Convergence criteria met. Covariance Parameter Estimates (REML) Cov Parm Ratio Estimate Std Error Z Pr > |Z| VARIETY 1.28336110 0.07315541 0.07801948 0.94 0.3484 Residual 1.00000000 0.05700298 0.02713246 2.10 0.0356 Model Fitting Information for Y Description Value Observations 13.0000 Variance Estimate 0.0570 Standard Deviation Estimate 0.2388 REML Log Likelihood -3.5333 Akaike's Information Criterion -5.5333 Schwarz's Bayesian Criterion -6.0182 -2 REML Log Likelihood 7.0666 Solution for Random Effects Parameter Estimate SE Pred DDF T Pr > |T| VARIETY A 0.06380023 0.17189025 9 0.37 0.7191 VARIETY B -0.05130168 0.16734685 9 -0.31 0.7662 VARIETY C 0.28348211 0.16734685 9 1.69 0.1245 VARIETY D -0.29598066 0.17999023 9 -1.64 0.1345 ESTIMATE Statement Results Parameter Estimate Std Error DDF T Pr > |T| grand mean 3.98629530 0.15149809 3 26.31 0.0001 variety A 4.05009554 0.12672423 3 31.96 0.0001 variety B 3.93499362 0.11197054 3 35.14 0.0001 variety C 4.26977741 0.11197054 3 38.13 0.0001 variety D 3.69031464 0.14938108 3 24.70 0.0001