Extra Exercise 16
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(a) Supplementary Exercise 9.2: Prescription of tetracycline drugs.

One response variable (whether a doctor prescribed tetracycline drugs to
at least one patient under the age of 8, or not), and the three county
types form an explanatory varible with three categories. The statistical model 
is 3 independent binomial distributions (model I), one for each county type, 
and the assumptions correspond to 3 independent binomial settings.


(b) Supplementary Exercise 9.4: Nutrition and illness in children.

Two response variables (nutrition status (normal/I/II/III & IV) and
illness (URI/Diarrhea/ URI & diarrhea/None)) observed in 1165 preschool
children in India. Neither of them were known in advance for any of the 
children. The model is therefore a single multinomial on 4*4=16 categories
- the 16 possible combinations of the categories of the two variables.
This is the model II, or a model/design to test independence between
nutrition and illness status.


(c) Supplementary Exercise 9.22: Performance of a stuck fund.

Two response variables (the performances (winner/loser) of 240 stock funds
over two consecutive years). The design could also be termed two paired
samples because the same funds were observed twice. The interest is in
whether any (positive) association exists between the performances in
the two years, contrary to other situations with two paired samples
where agreement or comparison of proportions is of interest. The
statistical model is a single multinomial on 2*2=4 categories - model II
to test independence between the two variables, or in this case between
the performances in the two years.


(d) Supplementary Exercise 9.57: Treatment for cocaine addiction. 

One response variable (cocaine relapse no/yes), and three treatment groups
forming an explanatory variable. Note that the data table has been presented
with the explanatory variable along the rows, contrary to the examples
considered so far. So in this case the statistical model is a binomial
distribution for each row, and model I to compare independent
populations.