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 The RSREG Procedure

## Example 56.2: Response Surface Analysis with Covariates

One way of viewing covariates is as extra sources of variation in the dependent variable that may mask the variation due to primary factors. This example demonstrates the use of the COVAR= option in PROC RSREG to fit a response surface model to the dependent variable values corrected for the covariates.

You have a chemical process with a yield that you hypothesize to be dependent on three factors: reaction time, reaction temperature, and reaction pressure. You perform an experiment to measure this dependence. You are willing to include up to 20 runs in your experiment, but you can perform no more than 8 runs on the same day, so the design for the experiment is composed of three blocks. Additionally, you know that the grade of raw material for the reaction has a significant impact on the yield. You have no control over this, but you keep track of it. The following statements create a SAS data set containing the results of the experiment:

data Experiment;
input Day Grade Time Temp Pressure Yield;
datalines;
1 67      -1     -1     -1         32.98
1 68      -1      1      1         47.04
1 70       1     -1      1         67.11
1 66       1      1     -1         26.94
1 74       0      0      0        103.22
1 68       0      0      0         42.94
2 75      -1     -1      1        122.93
2 69      -1      1     -1         62.97
2 70       1     -1     -1         72.96
2 71       1      1      1         94.93
2 72       0      0      0         93.11
2 74       0      0      0        112.97
3 69       1.633  0      0         78.88
3 67      -1.633  0      0         52.53
3 68       0      1.633  0         68.96
3 71       0     -1.633  0         92.56
3 70       0      0      1.633     88.99
3 72       0      0     -1.633    102.50
3 70       0      0      0         82.84
3 72       0      0      0        103.12
;

Your first analysis neglects to take the covariates into account. The following statements use PROC RSREG to fit a response surface to the observed yield, but note that Day and Grade are omitted.

proc rsreg data=Experiment;
model Yield = Time Temp Pressure;
run;

The ANOVA results (shown in Output 56.2.1) indicate that no process variable effects are significantly larger than the background noise.

Output 56.2.1: Analysis of Variance Ignoring Covariates

 The RSREG Procedure

 Regression DF Type I Sum of Squares R-Square F Value Pr > F Linear 3 1880.842426 0.1353 0.67 0.5915 Quadratic 3 2370.438681 0.1706 0.84 0.5023 Crossproduct 3 241.873250 0.0174 0.09 0.9663 Total Model 9 4493.154356 0.3233 0.53 0.8226

 Residual DF Sum of Squares Mean Square Total Error 10 9405.129724 940.512972

However, when the yields are adjusted for covariate effects of day and grade of raw material, very strong process variable effects are revealed. The following statements produce the ANOVA results in Output 56.2.2. Note that in order to include the effects of the classification factor Day as covariates, you need to create dummy variables indicating each day separately.

data Experiment; set Experiment;
d1 = (Day = 1);
d2 = (Day = 2);
d3 = (Day = 3);
proc rsreg data=Experiment;
model Yield = d1-d3 Grade Time Temp Pressure / covar=4;
run;

Output 56.2.2: Analysis of Variance Including Covariates

 The RSREG Procedure

 Regression DF Type I Sum of Squares R-Square F Value Pr > F Covariates 3 13695 0.9854 316957 <.0001 Linear 3 156.524497 0.0113 3622.53 <.0001 Quadratic 3 22.989775 0.0017 532.06 <.0001 Crossproduct 3 23.403614 0.0017 541.64 <.0001 Total Model 12 13898 1.0000 80413.2 <.0001

 Residual DF Sum of Squares Mean Square Total Error 7 0.100820 0.014403

The results show very strong effects due to both the covariates and the process variables.

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