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Biostatistics 514/517
Autumn 2013
Homework 2
Problems 1-4 use data on Seattle air pollution; read the description of the dataset.
1.
a. Just using the data from the winter months (November through March), compute
for PM1: the mean, mode, median, 90th percentile, standard deviation, IQR. Also
compute the proportion of days with at least one hospital admission for respiratory
disease.
b. Repeat part (a) just using the data from the non-winter months (April through
October)
Since we are interested in comparing the descriptive statistics for the winter and non-winter
months, present the results in a way that facilitates the comparison.
2. Make a graph that displays the seasonal patterns in PM1.
3. Make a graph that displays the seasonal patterns in hospital admissions.
4. Particulate air pollution and hospital admissions have similar seasonal patterns and both
higher in the winter. Give some possible explanations for the similarity.
The Western Collaborative Group Study (WCGS), a prospective cohort study, recruited middleaged men (ages 39 to 59) who were employees of 10 California companies and collected data
on 3154 individuals during the years 1960-1961. These subjects were primarily selected to
study the relationship between behavior pattern and the risk of coronary heart disease (CHD).
A number of other risk factors were also measured to provide the best possible assessment of
the CHD risk associated with behavior type. Most variables in the dataset are self-explanatory;
here are some details:
age – years
height – inches
weight – pounds
sbp - systolic blood pressure
dbp - diastolic blood pressure
chol – total cholesterol
ncigs – number of cigarettes smoked per day
dibpat – “type A” or “type B” behavioral pattern
wikipedia.org:Type_A_and_Type_B_personality_theory
chd69 – indicator of whether a CHD event observed
typchd69 – type of CHD event
time169 - follow-up time (days)
arcus – presence of arcus senilis (you might need to look this up)
5. Provide appropriate univariate descriptive statistics for the following variables in the WCGS
dataset: age, height, weight, systolic blood pressure, diastolic blood pressure, total cholesterol,
dichotomous behavioral pattern, arcus, CHD event.
6. Use graphics and/or summary statistics to describe the distribution of follow-up time. Write
1-2 sentences describing the distribution of this variable.
7. Use graphics and/or summary statistics to describe the distribution of number of cigarettes
smoked per day. Write 1-2 sentences describing the distribution of this variable.
8. For the study’s primary predictor of interest (dichotomous behavioral pattern) and primary
outcome of interest (CHD), provide appropriate bivariate descriptives *ignoring* the fact that
different subjects have different times of observation.
9. Examine the distribution of follow-up time by dichotomous behavioral pattern. Speculate on
the reason why follow-up times might be different for “type A” and “type B” personality types.
Give at least two different possible reasons.