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 The VARCOMP Procedure

## Analyzing the Cure Rate of Rubber

This example, using data from Hicks (1973), concerns an experiment to determine the sources of variability in cure rates of rubber. The goal of the experiment was to find out if the different laboratories contributed more to the variance of cure rates than did the different batches of raw materials. This information would be useful in trying to control the cure rate of the final product because it would provide insights into the sources of the variability in cure rates. The rubber used was cured at three temperatures, which were taken to be fixed. Three laboratories were chosen at random, and three different batches of raw material were tested at each combination of temperature and laboratory. The following statements read the data into the SAS data set Cure.

```   title 'Analyzing the Cure Rate of Rubber';
data Cure;
input Lab Temp Batch \$ Cure @@;
datalines;
1 145 A 18.6   1 145 A 17.0   1 145 A 18.7   1 145 A 18.7
1 145 B 14.5   1 145 B 15.8   1 145 B 16.5   1 145 B 17.6
1 145 C 21.1   1 145 C 20.8   1 145 C 21.8   1 145 C 21.0
1 155 A  9.5   1 155 A  9.4   1 155 A  9.5   1 155 A 10.0
1 155 B  7.8   1 155 B  8.3   1 155 B  8.9   1 155 B  9.1
1 155 C 11.2   1 155 C 10.0   1 155 C 11.5   1 155 C 11.1
1 165 A  5.4   1 165 A  5.3   1 165 A  5.7   1 165 A  5.3
1 165 B  5.2   1 165 B  4.9   1 165 B  4.3   1 165 B  5.2
1 165 C  6.3   1 165 C  6.4   1 165 C  5.8   1 165 C  5.6
2 145 A 20.0   2 145 A 20.1   2 145 A 19.4   2 145 A 20.0
2 145 B 18.4   2 145 B 18.1   2 145 B 16.5   2 145 B 16.7
2 145 C 22.5   2 145 C 22.7   2 145 C 21.5   2 145 C 21.3
2 155 A 11.4   2 155 A 11.5   2 155 A 11.4   2 155 A 11.5
2 155 B 10.8   2 155 B 11.1   2 155 B  9.5   2 155 B  9.7
2 155 C 13.3   2 155 C 14.0   2 155 C 12.0   2 155 C 11.5
2 165 A  6.8   2 165 A  6.9   2 165 A  6.0   2 165 A  5.7
2 165 B  6.0   2 165 B  6.1   2 165 B  5.0   2 165 B  5.2
2 165 C  7.7   2 165 C  8.0   2 165 C  6.6   2 165 C  6.3
3 145 A 19.7   3 145 A 18.3   3 145 A 16.8   3 145 A 17.1
3 145 B 16.3   3 145 B 16.7   3 145 B 14.4   3 145 B 15.2
3 145 C 22.7   3 145 C 21.9   3 145 C 19.3   3 145 C 19.3
3 155 A  9.3   3 155 A 10.2   3 155 A  9.8   3 155 A  9.5
3 155 B  9.1   3 155 B  9.2   3 155 B  8.0   3 155 B  9.0
3 155 C 11.3   3 155 C 11.0   3 155 C 10.9   3 155 C 11.4
3 165 A  6.7   3 165 A  6.0   3 165 A  5.0   3 165 A  4.8
3 165 B  5.7   3 165 B  5.5   3 165 B  4.6   3 165 B  5.4
3 165 C  6.6   3 165 C  6.5   3 165 C  5.9   3 165 C  5.8
;
```

The variables Lab, Temp, and Batch contain levels of laboratory, temperature, and batch, respectively. The Cure variable contains the response values.

The following SAS statements perform a restricted maximum-likelihood variance component analysis.

```   proc varcomp method=reml;
class Temp Lab Batch;
model Cure=Temp|Lab Batch(Lab Temp) / fixed=1;
run;
```

The FIXED=1 option indicates that the first factor, Temp, is fixed. The effect specification Temp|Lab is equivalent to putting the three terms Temp, Lab, and Temp*Lab in the model. Batch(Lab Temp) is equivalent to putting Batch(Temp*Lab) in the MODEL statement. The results of this analysis are displayed in Figure 69.1 through Figure 69.4.

 Analyzing the Cure Rate of Rubber

 Variance Components Estimation Procedure

 Class Level Information Class Levels Values Temp 3 145 155 165 Lab 3 1 2 3 Batch 3 A B C

 Number of observations 108

 Dependent Variable: Cure

Figure 69.1: Class Level Information

Figure 69.1 provides information about the variables used in the analysis and the number of observations and specifies the dependent variable.

 Analyzing the Cure Rate of Rubber

 Variance Components Estimation Procedure

 REML Iterations Iteration Objective Var(Lab) Var(Temp*Lab) Var(Batch(Temp*Lab)) Var(Error) 0 13.4500060254 0.5094464340 0 2.4004888633 0.5787185225 1 13.0898262160 0.3194348317 0 2.0869636935 0.6016005334 2 13.0893125570 0.3176048001 0 2.0738906134 0.6026217204 3 13.0893125555 0.3176017115 0 2.0738685461 0.6026234568

 Convergence criteria met.

Figure 69.2: Iteration History

The "REML Iterations" table, shown in Figure 69.2, displays the iteration history, which includes the value of the objective function associated with REML and the values of the variance components at each iteration.

 Analyzing the Cure Rate of Rubber

 Variance Components Estimation Procedure

 REML Estimates Variance Component Estimate Var(Lab) 0.31760 Var(Temp*Lab) 0 Var(Batch(Temp*Lab)) 2.07387 Var(Error) 0.60262

Figure 69.3: REML Estimates

Figure 69.3 displays the REML estimates of the variance components.

 Analyzing the Cure Rate of Rubber

 Variance Components Estimation Procedure

 Asymptotic Covariance Matrix of Estimates Var(Lab) Var(Temp*Lab) Var(Batch(Temp*Lab)) Var(Error) Var(Lab) 0.32452 0 -0.04998 1.026E-12 Var(Temp*Lab) 0 0 0 0 Var(Batch(Temp*Lab)) -0.04998 0 0.45042 -0.0022417 Var(Error) 1.026E-12 0 -0.0022417 0.0089668

Figure 69.4: Covariance Matrix for REML Estimates

The "Asymptotic Covariance Matrix of Estimates" table in Figure 69.4 displays the asymptotic covariance matrix of the REML estimates.

The results of the analysis show that the variance attributable to Batch(Temp*Lab) (with a variance component of 2.0739) is considerably larger than the variance attributable to Lab (0.3176). Therefore, attempts to reduce the variability of cure rates should concentrate on improving the homogeneity of the batches of raw material used rather than standardizing the practices or equipment within the laboratories. Also, note that since the Batch(Temp*Lab) variance is considerably larger than the experimental error (Var(Error)=0.6026), the Batch(Temp*Lab) variability plays an important part in the overall variability of the cure rates.

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