7. Compared to modeling BMI linearly, do you think modeling BMI with a quadratic term, a spline, or categories is more or less robust to non-linearity?
4

8. Is the constant variance assumption met in this model? Describe how you reached your conclusions, i.e. why or why not?
9. Was the assumption of independence met in this model? Yes.Describe how you reached your conclusions, i.e. why or why not?
5

10. Use median regression to evaluate whether the findings for BMI are robust to outliers (continue to use “other” as the race reference category). Compare your results (BMI estimate only) to the standard regression model.
Interpret the coefficient of BMI in the median regression.
Do you think outliers for BMI are driving the association between BMI and systolic blood pressure?
Please provide your SAS code and the SAS output of the models.
See Appendix.
11. Is there evidence of an interaction between bmi and alcohol consumption?
How did you test for the interaction? I added an interaction term for bmi and alcohol consumption, ran the model, and checked to see if p < 0.05 for the interaction term for the null hypothesis that β = 0.Write out your model.
9
*bmi*boozecat1
Please provide your SAS code and the SAS model output.
See Appendix.
6

Appendix for SAS code and abridged SAS model output.
Question 1
/* Q1 */
data
HW2;
set
"P:\Spring 2014\EPI204\hw2.sas7bdat"
;
bmi = wt/((height/
100
)*(height/
100
));
sex = sex -
1
;
boozecat = (booze =
0
);
run
;
Question 2
/* Q2 */
proc
glm
data
=hw2 ;
class
race / ref = last;
model
sbp = ageyrs bmi race sex smokever boozecat hdl /
solution
;
output
out
=hw2_2
predicted
=phat
residual
=resids;
run
;
The SAS System
The GLM Procedure
Dependent Variable: sbp
Source
DF
Sum of Squares
Mean Square
F Value
Pr > F
Model
8
1077805.962
134725.745
324.77 <.0001
Error
7478
3102111.733
414.832
Corrected Total
7486
4179917.695
R-Square
Coeff Var

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- Spring '14
- Hernandez-Diaz
- Statistics, Linear Regression, Regression Analysis, Standard Deviation, Variance