Biostatistics

486 CHAPTER 9 SIMPLE LINEAR REGRESSION AND CORRELATION
2. Refer to the data for 1050 subjects with essential hypertension (HYPERTEN). Suppose you are a
statistical consultant to a medical research team interested in essential hypertension. Select a
simple random sample from the population and perform the analyses that you think would be
useful to the researchers. Present your findings and conclusions in narrative form and illustrate
with graphs where appropriate. Compare your results with those of your classmates. Consult with
your instructor regarding the size of sample you should select.
3. Refer to the data for 1200 patients with rheumatoid arthritis (CALCIUM). One hundred patients
received the medicine at each dose level. Suppose you are a medical researchers wishing to gain
insight into the nature of the relationship between dose level of prednisolone and total body
calcium. Select a simple random sample of three patients from each dose level group and do the
following.
(a) Use the total number of pairs of observations to obtain the least-squares equation describing
the relationship between dose level (the independent variable) and total body calcium.
(b) Draw a scatter diagram of the data and plot the equation.
(c) Compute r and test for significance at the .05 level. Find the p value.
(d) Compare your results with those of your classmates.
REFERENCES
Methodology References
1. FREDERICK HARTWIG with BRAIN E. DEARING, Exploratory Data Analysis, Sage Publications, Beverly Hills, 1979.
2. IAIN M. JOHNSTONE and PAUL F. VELLEMAN, “The Resistant Line and Related Regression Methods,” Journal of the
American Statistical Association, 80 (1985), 1041–1054.
3. DONALD R. MCNEIL, Interactive Data Analysis: A Practical Primer, Wiley, New York, 1977.
4. PAUL F. VELLEMAN and DAVID C. HOAGLIN, Applications, Basics, and Computing of Exploratory Data Analysis,
Duxbury, Belmont, CA, 1981.
5. R. A. FISHER, “On the Probable Error of a Coefficient of Correlation Deduced from a Small Sample,” Metron, 1
(1921), 3–21.
6. H. HOTELLING, “New Light on the Correlation Coefficient and Its Transforms,” Journal of the Royal Statistical
Society, Series B, 15 (1953), 193–232.
7. TERRY E. DIELMAN, Applied Regression Analysis for Business and Economics, Second Edition, Duxbury, Belmont,
CA, 1996.
8. RONALD R. HOCKING, Methods and Applications of Linear Models: Regression and the Analysis of Variance,
Wiley, New York, 1996.
9. WILLIAM MENDENHALL and TERRY SINCICH, A Second Course in Statistics: Regression Analysis, Fifth Edition,
Prentice Hall, Upper Saddle River, NJ, 1996.
10. JOHN NETER, MICHAEL H. KUTNER, CHRISTOPHER J. NACHTSHEIM, and WILLIAM WASSERMAN, Applied Linear
Regression Models, Third Edition, Irwin, Chicago, 1996.
Applications References
A-1.
A-2.
A-3.
JEAN-PIERRE DESPRES, DENIS PRUD’HOMME, MARIE-CHRISTINE POULIOT, ANGELO TREMBLAY, and CLAUDE BOUCHARD,
“Estimation of Deep Abdominal Adipose-Tissue Accumulation from Simple Anthropometric Measurements in
Men,” American Journal of Clinical Nutrition, 54 (1991), 471–477.
MORI J. KRANTZ, ILANA B. KUTINSKY, ALASTAIR D. ROBERSTON, and PHILIP S. MEHLER, “Dose-Related Effects of
Methadone on QT Prolongation in a Series of Patients with Torsade de Pointes,” Pharmacotherapy, 23 (2003),
802–805.
ROBERT A. REISS,CURTIS E. HAAS,DEBORAH L. GRIFFIS,BERNADETTE PORTER, and MARYANN TARA, “Point-of-Care
Versus Laboratory Monitoring of Patients Receiving Different Anticoagulant Therapies,” Pharmacotherapy, 22
(2002), 677–685.