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Use SAS output to answer the question.

b. Based on the model you obtained from the previous section, determine the adjusted SBP means for smokers and nonsmokers (Hint: In this model, the adjusted SBP means are means of SBP for smokers and nonsmokers at fixed values of QUET and AGE BINARY. (1) For the continuous variable QUET: find the overall sample mean of QUET, (2) For the binary variable AGE_BINARY, find the mode of AGE_BINARY (you can use SAS Proc Freq to get the mode of AGE_BINARY as shown below). Remember that mode is defined as the level of AGE_BINARY with the highest frequency. Proc Freq data=HW3001; table AGE BINARY, run Then, report the adjusted SBP means for smokers and non-smokers at the mean value of QUET and mode of AGE_BINARY. Test whether the true adjusted mean SBP- QUET relationship is the parallel between the two age groups within each smoking status. I The SAS System The REG Procedure Model: MODEL1 Dependent Variable: SBP Adjusted R-Square Selection Method Number of Observations Read 32 Number of Observations Used 32 Number in Adjusted Model R-Square R-Square C(p) Variables in Model 3 0.7353 0.7609 4.0000 QUET AGE SMK 2. 0.7112 0.7298 5.6481 AGE SMK 2. 0.6165 0.6412 16.0212 QUET AGE N 0.6165 0.6412 16 0253 QUET SMK 1 0.5876 0 6009 18.7414 AGE 1 0.5356 0 5506 24 6414 QUE 1 0.0299 0.0612 81.9640 SMK Forward Selection: Step 2 Variable SMK Entered: R-Square = 0.7298 and C(p) = 5.6481 Analysis of Variance Source Sum of Mean DE Squares Square F Value Pr>F 2 4689.68423 2344.84211 39.16 F Intercept 48.04960 11.12956 1115.95464 18.64 0.0002 AGE 1.70916 0.20176 4296.58607 71.76 F 1 AGE 1 0.6009 0.6009 18.7414 45.18 F 2 4689.68423 2344.84211 39.16 F Intercept 48.04960 11.12956 1115.95464 18.64 0.0002 AGE 1.70916 0.20176 4296.58607 71.76 F 1 AGE 1 0.6009 0.6009 18.7414 45.18