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    The SAS System

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    durga03
    This document contains the output from running several SAS procedures to analyze a dataset containing measurements (y) taken at different temperatures (temp) and types. The GLM procedure was used to fit a linear model with interactions and found the type and temp effects as well as their interaction to be significant. The Mixed procedure was then used to fit the same linear mixed model and obtain covariance parameter estimates. Diagnostic plots of the residuals versus predicted values and a normal Q-Q plot were also produced.

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    The SAS System

    Uploaded by

    durga03
    22 views8 pages
    This document contains the output from running several SAS procedures to analyze a dataset containing measurements (y) taken at different temperatures (temp) and types. The GLM procedure was used to fit a linear model with interactions and found the type and temp effects as well as their interaction to be significant. The Mixed procedure was then used to fit the same linear mixed model and obtain covariance parameter estimates. Diagnostic plots of the residuals versus predicted values and a normal Q-Q plot were also produced.

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    This document contains the output from running several SAS procedures to analyze a dataset containing measurements (y) taken at different temperatures (temp) and types. The GLM procedure was used to fit a linear model with interactions and found the type and temp effects as well as their interaction to be significant. The Mixed procedure was then used to fit the same linear mixed model and obtain covariance parameter estimates. Diagnostic plots of the residuals versus predicted values and a normal Q-Q plot were also produced.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as DOC, PDF, TXT or read online from Scribd
    Download as doc, pdf, or txt
    22 views8 pages

    The SAS System

    Uploaded by

    durga03
    This document contains the output from running several SAS procedures to analyze a dataset containing measurements (y) taken at different temperatures (temp) and types. The GLM procedure was used to fit a linear model with interactions and found the type and temp effects as well as their interaction to be significant. The Mixed procedure was then used to fit the same linear mixed model and obtain covariance parameter estimates. Diagnostic plots of the residuals versus predicted values and a normal Q-Q plot were also produced.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as DOC, PDF, TXT or read online from Scribd
    Download as doc, pdf, or txt
    of 8

    07:53 Wednesday, May 15, 2013 1

    The SAS System


    The GLM Procedure
    Class Level Information Class type temp LevelsValues
    31 2 3 31 2 3

    Number of Observations Read Number of Observations Used

    36 36

    07:53 Wednesday, May 15, 2013 2

    The SAS System


    The GLM Procedure Dependent Variable: y
    Sum of Squares Mean Square F Value Pr > F
    7427.02778 675.21296 11.00 <.0001

    Source Model Error Corrected Total

    DF

    8 59416.22222 27 18230.75000 35 77646.97222

    R-Square Coeff Var Root MSE y Mean


    0.765210 24.62372 25.98486 105.5278

    Source type temp type*temp

    DF

    Type I SS Mean Square F Value Pr > F


    5341.86111 19559.36111 2403.44444 7.91 28.97 3.56 0.0020 <.0001 0.0186

    2 10683.72222 2 39118.72222 4 9613.77778

    07:53 Wednesday, May 15, 2013 3

    The SAS System


    The GLM Procedure Dependent Variable: y
    Source type temp type*temp DF Type III SS Mean Square F Value Pr > F
    2 10683.72222 2 39118.72222 4 9613.77778 5341.86111 19559.36111 2403.44444 7.91 28.97 3.56 0.0020 <.0001 0.0186

    07:53 Wednesday, May 15, 2013 4

    The SAS System


    The Mixed Procedure
    Model Information Data Set Dependent Variable Covariance Structure Estimation Method Residual Variance Method Fixed Effects SE Method
    WORK.LIFE y Diagonal REML Profile Model-Based

    Degrees of Freedom Method Residual

    Class Level Information Class type temp LevelsValues


    31 2 3 31 2 3

    Dimensions Covariance Parameters Columns in X Columns in Z Subjects Max Obs Per Subject
    1 16 0 1 36

    Number of Observations Number of Observations Read Number of Observations Used Number of Observations Not Used
    36 36 0

    Covariance Parameter Estimates Cov Parm Residual Estimate


    675.21

    07:53 Wednesday, May 15, 2013 5

    The SAS System


    The Mixed Procedure
    Fit Statistics -2 Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better)
    265.0 267.0 267.2 268.3

    Type 3 Tests of Fixed Effects Effect type temp type*temp Num Den DF DF F Value Pr > F
    2 2 4 27 27 27 7.91 28.97 3.56 0.0020 <.0001 0.0186

    07:53 Wednesday, May 15, 2013 6

    Normal Probability Plot


    Residual
    50 40 30 20 10 0 -10 -20 -30 -40 -50 -60 -70 -3 -2 -1 0 1 2 3

    Rank for Variable Resid

    07:53 Wednesday, May 15, 2013 7

    plot of Residual VersusPredicted Means


    Residual
    50 40 30 20 10 0 -10 -20 -30 -40 -50 -60 -70 40 50 60 70 80 90 100 110 120 130 140 150 160

    Predicted

    07:53 Wednesday, May 15, 2013 8

    ode: ods rtf file='assignmentq1.rtf'; data chemicalprocess;

    change this inputs(type n temp) as songsang n c the interaction graph.


    input BurningTimeInMinute@; input PressureInPsi@; do i=1 to 4; input y@; output; end; cards; 10 1 74 64 60 10 2 79 68 73 10 3 82 88 92 10 4 99 104 96 20 1 92 86 88 20 2 98 104 88 20 3 102 108 95 20 4 104 110 99 30 1 99 98 102 30 2 104 103 95 30 3 108 110 95 30 4 114 121 107 ; proc glm; class BurningTimeInMinute PressureInPsi; model y=B|temp; proc mixed data=life; class type temp; model y=type|temp / outp=lifeout; run; proc rank data=lifeout out=rankout normal=blom; var resid; ranks rank1; run; proc gplot data=rankout; plot resid*rank1/vaxis=axis1 haxis=axis2 frame; title "Normal Probability Plot"; symbol1 v=dot h=1 cv=green; run; proc gplot datlifeout; plot resid*pred/vaxis=axis1 haxis=axis2 frame; title "plot of Residual Versus Predicted Means"; symbol1 v=dot h=1 cv=red; run; ods rtf close; 0

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