From the Introduction to Modern Statistics (IMS) textbook, complete the following exercises:
\(~\)
Additionally, complete the following exercise using
R
:
Question #1: For this question you’ll use the data provided below, which are a random sample of \(n=200\) ICU patients from a research hospital affiliated with Carnegie Mellon University (CMU).
These data contain several categorical variables that are encoded numerically. The questions below will involve the variables:
Part A: According US
Census data, the population of the Pittsburgh, PA metropolitan area
(where CMU is located) is 85% white, 8% black, and 7% other races. Based
upon this information, do any racial groups appear to be
disproportionately represented among the ICU patients at this hospital?
Your answer should: clearly state a null hypothesis, provide a table of
expected counts, provide the test statistic, provide the p-value, and
make a conclusion. You are welcome to use R
functions to
obtain any or all of these required items.
Part B: Using a Chi-Squared test, do these data
provide statistical evidence of a difference in survival depending upon
the level of consciousness of a patient arriving at the ICU? Your answer
should: Your answer should: clearly state a null hypothesis, provide a
table of expected counts, provide the test statistic, provide the
p-value, and make a conclusion. You are welcome to use R
functions to obtain any or all of these required items.
Part C: Consider the hypothesis test you performed in Part B. Based upon the table of expected counts do you believe this test to be a statistically reliable choice? That is, would a statistician have an issue with using this particular hypothesis test given the assumptions it requires?
Part D: Repeat the hypothesis test in Part B using Fisher’s exact test. How does the \(p\)-value differ from the one you found in Part B?
Part E: Use these data to evaluate whether it is
statistically plausible that all three recorded levels of consciousness
are equally likely among new arrivals to the ICU. Your answer should:
clearly state a null hypothesis, provide a table of expected counts,
provide the test statistic, provide the p-value, and make a conclusion.
You are welcome to use R
functions to obtain any or all of
these required items.