# Biostatistics

**1. **The SAS dataset “112211” on blackboard gives the
systolic blood pressure (SBP), body size (QUET), age (AGE), and smoking history
(SMK = 0 if a nonsmoker, SMK = 1 if a current or previous smoker) for a
hypothetical sample of 32 white males over 40 years old from the town of
Angina. (QUET stands for “quetelet index,” a measure of size defined by QUET =
100 (weight/height2).)

**a. **On the accompanying scatter plot for QUET vs. AGE,
sketch by eye a line that fits the data reasonably well.
Comment on the relationship described.

** **

**b. **(1)

Determine the least-squares estimates of the slope ( 1) and intercept ( 0) for the

straight-line regression of QUET (*Y*) on AGE (X).

. (2) Sketch the estimated regression line on the scatter diagram in part (a) involving QUET and AGE. Compare this new line with the line you drew in part (a).

.
(3) Test the null hypothesis of zero
slope; be sure to **interpret **the result.

. (4) Based on your test in part (b)(3), would you conclude that body size increases

as age increases? **Explain **your answer.

.
(5) Obtain a **95% **confidence
interval for 1. In1terpret your result.

.
(6) Would you reject the null hypothesis *H0*:
= 0 in part (b)(3) based on the confidence

interval that you calculated in part (b)(5)? Explain. (7) Use SAS to find and draw 95% confidence bands and prediction bands with fitted

line on the scatter plot. (Must enable html output option in SAS: Tools->Options ->Preferences->Results->Check “Create HTML”.)

1

(8) Find the **99% confidence interval and prediction
interval **for an individual with AGE = 45 **from SAS output**. **Interpret
**your answer.
(9)
Determine r and r2, and interpret your results, **respectively**.
(10)
Test H0: ρ = 0 versus H1: ρ ≠ 0 at **α = .01**, **respectively**.
(Calculate by hand).

*No answers yet*