EXST7015 : Statistical Techniques II Geaghan Polynomial Regression #2 SAS example Page 1 12d-MReg-Polynomial Marathons.docs 1 ***********************************************; 2 *** Finish times in a 10 K race ***; 3 *** Data taken from various sites on the ***; 4 *** internet reporting race results ***; 5 ***********************************************; 6 options ps=256 ls=80 nocenter nodate nonumber; 7 8 data one; length hometown $ 23 sex $ 3; 9 infile 9 ! "C:\Geaghan\EXST\EXST7015New\Spring2003\SAS\12q-MReg-Polynomial 9 ! Marathons.DAT" missover; 10 TITLE1 'EXST7015: Marathon Footrace Example'; 11 input Marathon $ Age Sex $ TIME HomeTown $ 35-57; 12 if age eq 99 then age = .; 13 *(apparently 99 represents missing for 5 PA race participants); 14 *---+----1----+----2----+----3----+----4----+----5----+----6; 15 cards; NOTE: The infile "C:\Geaghan\EXST\EXST7015New\Spring2003\SAS\12q-MReg-Polynomial Marathons.DAT" is: File Name=C:\Geaghan\EXST\EXST7015New\Spring2003\SAS\12q-MReg-Polynomial Marathons.DAT, RECFM=V,LRECL=256 NOTE: 6150 records were read from the infile "C:\Geaghan\EXST\EXST7015New\Spring2003\SAS\12q-MReg-Polynomial Marathons.DAT". The minimum record length was 29. The maximum record length was 57. NOTE: The data set WORK.ONE has 6150 observations and 5 variables. NOTE: DATA statement used: real time 0.19 seconds cpu time 0.14 seconds 15 ! run; 16 ; 17 18 data two; set one; if marathon = 'VT052002'; NOTE: There were 6150 observations read from the data set WORK.ONE. NOTE: The data set WORK.TWO has 1490 observations and 5 variables. NOTE: DATA statement used: real time 0.05 seconds cpu time 0.05 seconds 19 proc sort data=two; by sex; RUN; NOTE: There were 1490 observations read from the data set WORK.TWO. NOTE: The data set WORK.TWO has 1490 observations and 5 variables. NOTE: PROCEDURE SORT used: real time 0.07 seconds cpu time 0.07 seconds 20 21 proc mixed data=two method=type1; BY sex; 22 TITLE2 'Quartic model - separate by sex'; 23 model time= age age*age age*age*age age*age*age*age 24 / htype=1 3 DDFM=Satterthwaite solution; 25 run; NOTE: The PROCEDURE MIXED printed pages 1-2. NOTE: PROCEDURE MIXED used: real time 0.13 seconds cpu time 0.12 secondsEXST7015 : Statistical Techniques II Geaghan Polynomial Regression #2 SAS example Page 2 12d-MReg-Polynomial Marathons.docs EXST7015: Marathon Footrace Example Quartic model - separate by sex sex=F The Mixed Procedure Model Information Data Set WORK.TWO Dependent Variable TIME Covariance Structure Diagonal Estimation Method Type 1 Residual Variance Method Factor Fixed Effects SE Method Model-Based Degrees of Freedom Method Residual Dimensions Covariance Parameters 1 Columns in X 5 Columns in Z 0 Subjects 1 Max Obs Per Subject 527 Observations Used 527 Observations Not Used 0 Total Observations 527 Type 1 Analysis of Variance Sum of Error Source DF Squares Mean Square Expected Mean Square Error Term DF F Value Pr > F Age 1 4869.217307 4869.217307 Var(Residual) + MS(Residual) 522 8.34 0.0040 Q(Age,Age*Age,Age*Age* Age,Age*Age*Age*Age) Age*Age 1 3535.615012 3535.615012 Var(Residual) + MS(Residual) 522 6.06 0.0142 Q(Age*Age,Age*Age*Age, Age*Age*Age*Age) Age*Age*Age 1 262.700283 262.700283 Var(Residual) + MS(Residual) 522 0.45 0.5026 Q(Age*Age*Age,Age*Age*Age*Age) Age*Age*Age*Age 1 1.495088 1.495088 Var(Residual) + Q(Age*Age*Age*Age) MS(Residual) 522 0.00 0.9597 Residual 522 304684 583.686674 Var(Residual) . . . .EXST7015 : Statistical Techniques II Geaghan Polynomial Regression #2 SAS example Page 3 12d-MReg-Polynomial Marathons.docs Covariance Parameter Estimates Cov Parm Estimate Residual 583.69 Fit Statistics -2 Res Log Likelihood 4884.3 AIC (smaller is better) 4886.3 AICC (smaller is better) 4886.3 BIC (smaller is better) 4890.5 Solution for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| Intercept 294.52 128.80 522 2.29 0.0226 Age -3.6313 14.4413 522 -0.25 0.8016 Age*Age 0.07038 0.5872 522 0.12 0.9046 Age*Age*Age -0.00010 0.01026 522 -0.01 0.9922 Age*Age*Age*Age -3.3E-6 0.000065 522 -0.05 0.9597 Type 1 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F Age 1 522 8.34 0.0040 Age*Age 1 522 6.06 0.0142 Age*Age*Age 1 522 0.45 0.5026 Age*Age*Age*Age 1 522 0.00 0.9597 Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F Age 1 522 0.06 0.8016 Age*Age 1 522 0.01 0.9046 Age*Age*Age 1 522 0.00 0.9922 Age*Age*Age*Age 1 522 0.00 0.9597EXST7015 : Statistical Techniques II Geaghan Polynomial Regression #2 SAS example Page 4 12d-MReg-Polynomial Marathons.docs EXST7015: Marathon Footrace Example Quartic model -